{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "## Subsurface Data Analytics \n",
    "\n",
    "### Basic Univariate Distributions in Python \n",
    "\n",
    "#### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic Univariate Data Distribution Plotting in Python with GeostatsPy\n",
    "\n",
    "Here's a simple workflow with some basic univariate statistics and distribution plotting of tabular (easily extended to gridded) data summary statistics and distributions. This should help you get started data visualization and interpretation.\n",
    "\n",
    "#### Objective \n",
    "\n",
    "The objective is to remove the hurdles of subsurface modeling workflow construction by providing building blocks and sufficient examples. This is not a coding class per se, but we need the ability to 'script' workflows working with numerical methods.    \n",
    "\n",
    "#### Getting Started\n",
    "\n",
    "Here's the steps to get setup in Python with the GeostatsPy package:\n",
    "\n",
    "1. Install Anaconda 3 on your machine (https://www.anaconda.com/download/). \n",
    "2. From Anaconda Navigator (within Anaconda3 group), go to the environment tab, click on base (root) green arrow and open a terminal. \n",
    "3. In the terminal type: pip install geostatspy. \n",
    "4. Open Jupyter and in the top block get started by copy and pasting the code block below from this Jupyter Notebook to start using the geostatspy functionality. \n",
    "\n",
    "You will need to copy the data files to your working directory.  They are avaiable here:\n",
    "\n",
    "1. Tabular data - sample_data.csv at https://git.io/fh4gm\n",
    "2. Gridded data - AI_grid.csv at https://git.io/fh4gU\n",
    "\n",
    "There are exampled below with these functions. You can go here to see a list of the available functions, https://git.io/fh4eX, other example workflows and source code. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geostatspy.GSLIB as GSLIB          # GSLIB utilies, visualization and wrapper\n",
    "import geostatspy.geostats as geostats    # GSLIB methods convert to Python        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also need some standard packages. These should have been installed with Anaconda 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np                        # ndarrys for gridded data\n",
    "import pandas as pd                       # DataFrames for tabular data\n",
    "import os                                 # set working directory, run executables\n",
    "import matplotlib.pyplot as plt           # for plotting\n",
    "from scipy import stats                   # summary statistics\n",
    "import seaborn as sns                     # advanced plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the working directory\n",
    "\n",
    "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#os.chdir(\"c:/PGE383\")             # set the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading Tabular Data\n",
    "\n",
    "Here's the command to load our comma delimited data file in to a Pandas' DataFrame object.  For fun try misspelling the name. You will get an ugly, long error.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'sample_data_cow.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[1;32mIn [4]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msample_data_cow.csv\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py:311\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    305\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[0;32m    306\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m    307\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39marguments),\n\u001b[0;32m    308\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[0;32m    309\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mstacklevel,\n\u001b[0;32m    310\u001b[0m     )\n\u001b[1;32m--> 311\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:680\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[0;32m    665\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m    666\u001b[0m     dialect,\n\u001b[0;32m    667\u001b[0m     delimiter,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    676\u001b[0m     defaults\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdelimiter\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[0;32m    677\u001b[0m )\n\u001b[0;32m    678\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m--> 680\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:575\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    572\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m    574\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 575\u001b[0m parser \u001b[38;5;241m=\u001b[39m TextFileReader(filepath_or_buffer, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m    577\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[0;32m    578\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
      "File \u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:933\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    930\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m    932\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m--> 933\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:1217\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m   1213\u001b[0m     mode \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1214\u001b[0m \u001b[38;5;66;03m# error: No overload variant of \"get_handle\" matches argument types\u001b[39;00m\n\u001b[0;32m   1215\u001b[0m \u001b[38;5;66;03m# \"Union[str, PathLike[str], ReadCsvBuffer[bytes], ReadCsvBuffer[str]]\"\u001b[39;00m\n\u001b[0;32m   1216\u001b[0m \u001b[38;5;66;03m# , \"str\", \"bool\", \"Any\", \"Any\", \"Any\", \"Any\", \"Any\"\u001b[39;00m\n\u001b[1;32m-> 1217\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[call-overload]\u001b[39;49;00m\n\u001b[0;32m   1218\u001b[0m \u001b[43m    \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1219\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1220\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1221\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1222\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1223\u001b[0m \u001b[43m    \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1224\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1225\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1226\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1227\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1228\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
      "File \u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\common.py:789\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    784\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m    785\u001b[0m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[0;32m    786\u001b[0m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[0;32m    787\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[0;32m    788\u001b[0m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[1;32m--> 789\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[0;32m    790\u001b[0m \u001b[43m            \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    791\u001b[0m \u001b[43m            \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    792\u001b[0m \u001b[43m            \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    793\u001b[0m \u001b[43m            \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    794\u001b[0m \u001b[43m            \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    795\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    796\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    797\u001b[0m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[0;32m    798\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'sample_data_cow.csv'"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('sample_data_cow.csv')     # load our data table (wrong name!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's Python, but there's method to the madness. In general the error shows a trace from the initial command into all the nested programs involved until the actual error occured. If you are debugging code (I know, I'm getting ahead of myself now), this is valuable for the detective work of figuring out what went wrong. I've spent days in C++ debugging one issue, this helps. So since you're working in Jupyter Notebook, the program just assumes you code. Fine. If you scroll to the bottom of the error you often get a summary statement *FileNotFoundError: File b'sample_data_cow.csv' does not exist*. Ok, now you know that you don't have a file iwth that name in the working directory.  \n",
    "\n",
    "Painful to leave that error in our workflow, eh? Everytime I passes it while making this documented I wanted to fix it. Its a coder thing... go ahead and erase it if you like. Just select the block and click on the scissors above in the top bar of this window. While we are at it, notice if you click the '+' you can add in a new block anywhere. Ok, let's spell the file name correctly and get back to work, already."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df = pd.read_csv('sample_data.csv')     # load our data table\n",
    "df = pd.read_csv('https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/sample_data.csv') # load data from Dr. Pyrcz's github repository"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No error now! It worked, we loaded our file into our DataFrame called 'df'. But how do you really know that it worked? Visualizing the DataFrame would be useful and we already leard about these methods in this demo (https://git.io/fNgRW). \n",
    "\n",
    "We can preview the DataFrame by printing a slice or by utilizing the 'head' DataFrame member function (with a nice and clean format, see below). With the slice we could look at any subset of the data table and with the head command, add parameter 'n=13' to see the first 13 rows of the dataset.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       X      Y  Facies  Porosity       Perm           AI\n",
      "0  100.0  900.0     1.0  0.100187   1.363890  5110.699751\n",
      "1  100.0  800.0     0.0  0.107947  12.576845  4671.458560\n",
      "2  100.0  700.0     0.0  0.085357   5.984520  6127.548006\n",
      "3  100.0  600.0     0.0  0.108460   2.446678  5201.637996\n",
      "4  100.0  500.0     0.0  0.102468   1.952264  3835.270322\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100187</td>\n",
       "      <td>1.363890</td>\n",
       "      <td>5110.699751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.107947</td>\n",
       "      <td>12.576845</td>\n",
       "      <td>4671.458560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.085357</td>\n",
       "      <td>5.984520</td>\n",
       "      <td>6127.548006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.108460</td>\n",
       "      <td>2.446678</td>\n",
       "      <td>5201.637996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.102468</td>\n",
       "      <td>1.952264</td>\n",
       "      <td>3835.270322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>100.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.110579</td>\n",
       "      <td>3.691908</td>\n",
       "      <td>5295.267191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>100.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.088936</td>\n",
       "      <td>1.073582</td>\n",
       "      <td>6744.996106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>100.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.102094</td>\n",
       "      <td>2.396189</td>\n",
       "      <td>5947.338115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.137453</td>\n",
       "      <td>5.727603</td>\n",
       "      <td>5823.241783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>200.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.137062</td>\n",
       "      <td>14.771314</td>\n",
       "      <td>5621.146994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>200.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.125984</td>\n",
       "      <td>10.675436</td>\n",
       "      <td>4292.700500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>200.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.121754</td>\n",
       "      <td>3.085825</td>\n",
       "      <td>5397.400218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>200.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.095147</td>\n",
       "      <td>0.962565</td>\n",
       "      <td>4619.786478</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        X      Y  Facies  Porosity       Perm           AI\n",
       "0   100.0  900.0     1.0  0.100187   1.363890  5110.699751\n",
       "1   100.0  800.0     0.0  0.107947  12.576845  4671.458560\n",
       "2   100.0  700.0     0.0  0.085357   5.984520  6127.548006\n",
       "3   100.0  600.0     0.0  0.108460   2.446678  5201.637996\n",
       "4   100.0  500.0     0.0  0.102468   1.952264  3835.270322\n",
       "5   100.0  400.0     0.0  0.110579   3.691908  5295.267191\n",
       "6   100.0  300.0     0.0  0.088936   1.073582  6744.996106\n",
       "7   100.0  200.0     0.0  0.102094   2.396189  5947.338115\n",
       "8   100.0  100.0     1.0  0.137453   5.727603  5823.241783\n",
       "9   200.0  900.0     1.0  0.137062  14.771314  5621.146994\n",
       "10  200.0  800.0     1.0  0.125984  10.675436  4292.700500\n",
       "11  200.0  700.0     0.0  0.121754   3.085825  5397.400218\n",
       "12  200.0  600.0     0.0  0.095147   0.962565  4619.786478"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.iloc[0:5,:])                   # display first 4 samples in the table as a preview\n",
    "df.head(n=13)                           # we could also use this command for a table preview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary Univariate Statistics for Tabular Data\n",
    "\n",
    "The table includes X and Y coordinates (meters), Facies 1 and 2 (1 is sandstone and 0 interbedded sand and mudstone), Porosity (fraction), permeability as Perm (mDarcy) and acoustic impedance as AI (kg/m2s*10^6). \n",
    "\n",
    "There are a lot of efficient methods to calculate summary statistics from tabular data in DataFrames. The describe command provides count, mean, minimum, maximum, and quartiles all in a nice data table. We use transpose just to flip the table so that features are on the rows and the statistics are on the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>261.000000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>261.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>629.823755</td>\n",
       "      <td>488.344828</td>\n",
       "      <td>0.620690</td>\n",
       "      <td>0.150357</td>\n",
       "      <td>183.711554</td>\n",
       "      <td>4203.657220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>341.200403</td>\n",
       "      <td>166.669352</td>\n",
       "      <td>0.486148</td>\n",
       "      <td>0.049783</td>\n",
       "      <td>344.959449</td>\n",
       "      <td>1317.753146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.058871</td>\n",
       "      <td>0.033611</td>\n",
       "      <td>1844.166880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>241.000000</td>\n",
       "      <td>416.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.104893</td>\n",
       "      <td>2.186525</td>\n",
       "      <td>2947.867713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>700.000000</td>\n",
       "      <td>479.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.137062</td>\n",
       "      <td>19.977020</td>\n",
       "      <td>4204.150893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>955.000000</td>\n",
       "      <td>539.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.199108</td>\n",
       "      <td>246.215865</td>\n",
       "      <td>5397.400218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1005.000000</td>\n",
       "      <td>989.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.242298</td>\n",
       "      <td>2642.999829</td>\n",
       "      <td>7881.898531</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 X           Y      Facies    Porosity         Perm  \\\n",
       "count   261.000000  261.000000  261.000000  261.000000   261.000000   \n",
       "mean    629.823755  488.344828    0.620690    0.150357   183.711554   \n",
       "std     341.200403  166.669352    0.486148    0.049783   344.959449   \n",
       "min      40.000000   29.000000    0.000000    0.058871     0.033611   \n",
       "25%     241.000000  416.000000    0.000000    0.104893     2.186525   \n",
       "50%     700.000000  479.000000    1.000000    0.137062    19.977020   \n",
       "75%     955.000000  539.000000    1.000000    0.199108   246.215865   \n",
       "max    1005.000000  989.000000    1.000000    0.242298  2642.999829   \n",
       "\n",
       "                AI  \n",
       "count   261.000000  \n",
       "mean   4203.657220  \n",
       "std    1317.753146  \n",
       "min    1844.166880  \n",
       "25%    2947.867713  \n",
       "50%    4204.150893  \n",
       "75%    5397.400218  \n",
       "max    7881.898531  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use a wide variety of statistical summaries built into NumPy's ndarrays.  When we use the command:\n",
    "```p\n",
    "df['Porosity']                       # returns an Pandas series\n",
    "df['Porosity'].values                # returns an ndarray\n",
    "```\n",
    "Panda's DataFrame returns all the porosity data as a series and if we add 'values' it returns a NumPy ndarray and we have access to a lot of NumPy methods. I also like to use the round function to round the answer to a limited number of digits for accurate reporting of precision and ease of reading.\n",
    "\n",
    "For example, now we could use commands. like this one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The minimum is 0.06.\n",
      "The maximum is 0.24.\n",
      "The mean is 0.15.\n",
      "The standard deviation is 0.05.\n"
     ]
    }
   ],
   "source": [
    "print('The minimum is ' + str(round((df['Porosity'].values).min(),2)) + '.')\n",
    "print('The maximum is ' + str(round((df['Porosity'].values).max(),2)) + '.')\n",
    "print('The mean is ' + str(round((df['Porosity'].values).mean(),2)) + '.')\n",
    "print('The standard deviation is ' + str(round((df['Porosity'].values).std(),2)) + '.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's some of the NumPy statistical functions that take ndarrays as an inputs.  With these methods if you had a multidimensional array you could calculate the average by row (axis = 1) or by column (axis = 0) or over the entire array (no axis specified). We just have a 1D ndarray so this is not applicable here.\n",
    "\n",
    "We calculate the inverse of the CDF, $F^{-1}_x(x)$ with Numpy percentile function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The minimum is 0.06\n",
      "The maximum is 0.24\n",
      "The range (maximum - minimum) is 0.18\n",
      "The P10 is 0.092\n",
      "The P50 is 0.137\n",
      "The P90 is 0.212\n",
      "The P13 is 0.095\n",
      "The median (P50) is 0.137\n",
      "The mean is 0.15\n"
     ]
    }
   ],
   "source": [
    "print('The minimum is ' + str(round(np.amin(df['Porosity'].values),2)))\n",
    "print('The maximum is ' + str(round(np.amax(df['Porosity'].values),2)))\n",
    "print('The range (maximum - minimum) is ' + str(round(np.ptp(df['Porosity'].values),2)))\n",
    "print('The P10 is ' + str(round(np.percentile(df['Porosity'].values,10),3)))\n",
    "print('The P50 is ' + str(round(np.percentile(df['Porosity'].values,50),3)))\n",
    "print('The P90 is ' + str(round(np.percentile(df['Porosity'].values,90),3)))\n",
    "print('The P13 is ' + str(round(np.percentile(df['Porosity'].values,13),3)))\n",
    "print('The median (P50) is ' + str(round(np.median(df['Porosity'].values),3)))\n",
    "print('The mean is ' + str(round(np.mean(df['Porosity'].values),3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can calculate the CDF value, $F_x(x)$, directly from the data.\n",
    "* we use a condition to creat a boolean array with the same size of the data and then count the cases that meet the condition\n",
    "* we are assuming equal weighting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cumulative probability for porosity = 0.1 is 0.18\n"
     ]
    }
   ],
   "source": [
    "value = 0.10\n",
    "cumul_prob = np.count_nonzero(df['Porosity'].values <= value)/len(df)\n",
    "print('The cumulative probability for porosity = ' + str(value) + ' is ' + str(round(cumul_prob,2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Weighted Univariate Statistics\n",
    "\n",
    "Later in the course we will talke about weights statistics. The NumPy command average allows for weighted averages as in the case of statistical expectation and declutered statistics. For demonstration, lets make a weighting array and apply it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The equal weighted average is 0.15, the same as the mean above.\n"
     ]
    }
   ],
   "source": [
    "nd = len(df)                              # get the number of data values\n",
    "wts = np.ones(nd)                         # make an array of nd length of 1's\n",
    "print('The equal weighted average is ' + str(round(np.average(df['Porosity'].values,weights = wts),3)) + ', the same as the mean above.')            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get fancy, we will modify the weights to be 0.5 if the porosity is greater than 13% and retain 1.0 if the porosity is less than or equal to 13%. The results should be a lower weighted average.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The equal weighted average is 0.112, lower than the equal weighted average above.\n"
     ]
    }
   ],
   "source": [
    "porosity = df['Porosity'].values\n",
    "wts[porosity > 0.13] *= 0.1\n",
    "print('The equal weighted average is ' + str(round(np.average(df['Porosity'].values,weights = wts),3)) + ', lower than the equal weighted average above.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I should note that SciPy stats functions provide a handy summary statistics function. The output is a 'list' of values (actually it is a SciPy.DescribeResult ojbect). One can extract any one of them to use in a workflow as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DescribeResult(nobs=261, minmax=(0.0588710426408954, 0.2422978845362023), mean=0.15035706160196555, variance=0.0024783238419715937, skewness=0.08071652694567066, kurtosis=-1.5618166076333853)\n",
      "Porosity kurtosis is -1.56\n"
     ]
    }
   ],
   "source": [
    "print(stats.describe(df['Porosity'].values))                # summary statistics   \n",
    "por_stats = stats.describe(df['Porosity'].values)           # store as an array\n",
    "print('Porosity kurtosis is ' + str(round(por_stats[5],2))) # extract a statistic "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Histograms\n",
    "\n",
    "Let's display some histograms. I reimplimented the hist function from GSLIB. See the parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function geostatspy.GSLIB.hist(array, xmin, xmax, log, cumul, bins, weights, xlabel, title, fig_name)>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GSLIB.hist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make a histogram for porosity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pormin = 0.05; pormax = 0.25\n",
    "GSLIB.hist(df['Porosity'].values,pormin,pormax,log=False,cumul = False,bins=10,weights = None, xlabel='Porosity (fraction)',title='Porosity Well Data',fig_name='hist_Porosity')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What's going on here? Looks quite bimodal. \n",
    "\n",
    "#### Histogram Bins, Number of Bins and Bin Size\n",
    "\n",
    "Let's explore with a few bins sizes to check the impact on the histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAACmo7vflOTfV5n1vCQ/niO47RsAzJtd01pwdz9pjVmPWqVtJ/mBacUCAADAbFTVOUmWuvudVZ6DAcD2N7WCKgAAADtbVd0lyTMzutx/o7bnJTkvSU4//fQpRwYAR26r76EKAADAznG/JPdJ8s6quiajBxC/vao+d2XD7r6ou/d2997du3dvcZgAMD5nqAIAADAV3f3uJJ9zaHwoqu7t7n+dWVAAsEnOUAUAAGAi1ng4MQAsFGeoAgAAMBHrPJz40PwztigUAJgaBVUAAOB2Z591ZvYvLU1seaft2ZNL9101seUBAMyagioAAHC7/UtLufbC4ye2vFMvmFxxFgBgHriHKgAAAADAmBRUAQAAAADGpKAKAAAAADAmBVUAAAAAgDEpqAIAAAAAjElBFQAAAABgTAqqAAAAAABjUlAFAAAAABiTgioAAAAAwJgUVAEAAAAAxqSgCgAAAAAwJgVVAAAAAIAxKagCAAAAAIxJQRUAAAAAYEwKqgAAAAAAY1JQBQAAAAAYk4IqAAAAAMCYFFQBAAAAAMakoAoAAAAAMCYFVQAAAACAMSmoAsAUVdVxVXVpVb2zqq6sqp8Zpt+nqt5WVe+rqpdV1bGzjhUAAICNKagCwHR9Kskju/tBSc5K8uiqemiS5yZ5Xnd/fpKPJHn67EIEAABgXAqqADBFPXJgGD1m+Okkj0zyimH6JUkev/XRAQAAcLgUVAFgyqrq6Kral+T6JK9N8k9JPtrdtw1N9ifZs8Zrz6uqy6rqshtuuGFL4gUAAGBtCqoAMGXd/enuPivJaUnOTvLAw3jtRd29t7v37t69e1ohAgAAMCYFVQDYIt390SSvT/IVSe5eVbuGWaclWZpVXAAAAIxPQRUApqiqdlfV3Yfhz07ydUmuzqiw+oSh2blJ/mQmAQIAAHBYdm3cBADYhFOSXFJVR2d0IPPl3f1nVXVVkj+oqp9L8o4kL5hlkAAAAIxHQRUApqi735XkwatMf39G91MFAABgG3HJPwAAAADAmBRUAQAA2LSqemFVXV9VVyyb9otV9Z6qeldV/fGh+4oDwHamoAoAAMAkXJzk0SumvTbJF3X3lyT5xyQ/udVBAcCkKagCAACwad39piT/vmLaX3X3bcPoW5OctuWBAcCEKagCAACwFb4zyV+sNbOqzquqy6rqshtuuGELwwKAw6OgCgAAwFRV1U8luS3JS9Zq090Xdffe7t67e/furQsOAA7TrlkHAAAAwOKqqqcm+aYkj+runnE4ALBpCqoAAABMRVU9OsmPJ/ma7v7ErOMBgElwyT8AAACbVlUvTfKWJA+oqv1V9fQkv57khCSvrap9VfVbMw0SACbAGaoAAABsWnc/aZXJL9jyQABgypyhCgAAAAAwJgVVAAAAAIAxzaSgWlU/UlVXVtUVVfXSqjququ5TVW+rqvdV1cuq6thZxAYAAAAAsJYtL6hW1Z4k/zXJ3u7+oiRHJ3likucmeV53f36SjyR5+lbHBgAAAACwnlld8r8ryWdX1a4kd0lyXZJHJnnFMP+SJI+fTWgAAAAAAKvb8oJqdy8l+aUk/5xRIfVjSS5P8tHuvm1otj/Jnq2ODQAAAABgPbO45P+kJOckuU+SU5Mcn+TRh/H686rqsqq67ODBnlKUAAAAAACfaRaX/H9tkg909w3dfWuSVyZ5WJK7D7cASJLTkiyt9uLuvqi793b33qOOqq2JGAAAAAAgsymo/nOSh1bVXaqqkjwqyVVJXp/kCUObc5P8yQxiAwAAAABY0yzuofq2jB4+9fYk7x5iuCjJTyT50ap6X5J7JnnBVscGAAAAALCeXRs3mbzuflaSZ62Y/P4kZ88gHACAuXDzTQdy6u4TJ7a80/bsyaX7rprY8uBITPpznfhsAwCzNZOCKgAAn+m2gwdz7YXHT2x5p16w6i3pYUtN+nOd+GwDALM1i3uoAgAAAABsSwqqAAAAAABjUlAFAAAAABiTgioAAAAAwJgUVAEAAAAAxqSgCgAAAAAwJgVVAAAAAIAxKagCAAAAAIxJQRUAAAAAYEwKqgAAAAAAY1JQBYApqqp7V9Xrq+qqqrqyqn54mP7sqlqqqn3Dz2NmHSsAAAAb2zXrAABgwd2W5Me6++1VdUKSy6vqtcO853X3L80wNgAAAA6TgioATFF3X5fkumH441V1dZI9s40KAACAI+WSfwDYIlV1RpIHJ3nbMOkHq+pdVfXCqjppdpEBAAAwLgVVANgCVXXXJH+U5PzuvjHJbya5X5KzMjqD9X+u8brzquqyqrrshhtu2KpwAQAAWIOCKgBMWVUdk1Ex9SXd/cok6e4Pd/enu/tgkt9OcvZqr+3ui7p7b3fv3b1799YFDQAAwKoUVAFgiqqqkrwgydXd/cvLpp+yrNk3J7liq2MDAADg8HkoFQBM18OSPCXJu6tq3zDtmUmeVFVnJekk1yT5nlkEBwAAwOFRUAWAKeruNyepVWa9ZqtjAQAAYPNc8g8AAMBEVNULq+r6qrpi2bR7VNVrq+q9w++TZhkjAGyWgioAAACTcnGSR6+Y9owkr+vu+yd53TAOANuWgioAAAAT0d1vSvLvKyafk+SSYfiSJI/fypgAYNIUVAEAAJimk7v7umH4Q0lOnmUwALBZCqoAAABsie7uJL3avKo6r6ouq6rLbrjhhi2ODADGp6AKAADANH24qk5JkuH39as16u6Luntvd+/dvXv3lgYIAIdDQRUAAIBpenWSc4fhc5P8yQxjAYBNU1AFAABgIqrqpUnekuQBVbW/qp6e5DlJvq6q3pvka4dxANi2ds06AAAAABZDdz9pjVmP2tJAAGCKnKEKAAAAADAmBVUAAAAAgDEpqAIAAAAAjMk9VAGAHePss87M/qWliS3vpgMHkhw/seUBAADzT0EVANgx9i8t5doLJ1cAPeH8Gye2LAAAYHtwyT8AAAAAwJgUVAEAAAAAxqSgCgAAAAAwJgVVAAAAAIAxKagCAAAAAIxJQRUAAAAAYEwKqgAAAAAAY1JQBQAAAAAY01gF1ar64mkHAgDzTj4EYKeQ8wBgbeOeofobVXVpVX1/VZ041YgAYH7JhwDsFHIeAKxhrIJqd39VkicnuXeSy6vq96vq66YaGQDMGfkQgJ1CzgPYGmefdWZO3X3i7T9nn3XmrENiDLvGbdjd762qn05yWZJfTfLgqqokz+zuV04rQACYJ/IhADuFnAcwffuXlnLthcffPn7qBUszjIZxjXsP1S+pqucluTrJI5M8trv/wzD8vCnGBwBzQz4EYKeQ8wBgbeOeofprSX4noyORNx+a2N3XDkcsAWAnkA8B2CnkPABYw7gF1W9McnN3fzpJquqoJMd19ye6+8VTiw4A5ot8CMBOIecBwBrGuuQ/yV8n+exl43cZph2Rqrp7Vb2iqt5TVVdX1VdU1T2q6rVV9d7h90lHunwAmJKJ5kMAmGNyHgCsYdyC6nHdfeDQyDB8l02s9/lJ/rK7H5jkQRndl+cZSV7X3fdP8rphHADmyaTzIQDMKzkPANYwbkH1pqr60kMjVfVlSW5ep/2aqurEJF+d5AVJ0t23dPdHk5yT5JKh2SVJHn8kyweAKZpYPgSAOSfnAcAaxr2H6vlJ/rCqrk1SST43ybcd4Trvk+SGJL9bVQ9KcnmSH05ycndfN7T5UJKTV3txVZ2X5LwkOfqoOsIQAOCInJ/J5UMAmGfnR84DgFWNVVDt7r+vqgcmecAw6R+6+9ZNrPNLk/xQd7+tqp6fFZf3d3dXVa8Ry0VJLkqSY485etU2ADANR5IPq+reSV6U0YHCTnJRdz+/qu6R5GVJzkhyTZJv7e6PTCt2ADgcE/4OCAALZdwzVJPkIRl96duV5EurKt39oiNY5/4k+7v7bcP4KzIqqH64qk7p7uuq6pQk1x/BsgFg2g43H96W5Me6++1VdUKSy6vqtUmemtG9w59TVc/IKBf+xHRDB4DDMqnvgACwUMYqqFbVi5PcL8m+JJ8eJndGZ9wclu7+UFX9S1U9oLv/Icmjklw1/Jyb5DnD7z853GUDwDQdST4cbmdz3TD88aq6OsmejO4d/vCh2SVJ3hAFVQDmxCS/AwLAohn3DNW9Sc7s7kldYv9DSV5SVccmeX+Sp2X0gKyXV9XTk3wwybdOaF0AMCmbyodVdUaSByd5W8a8dzgAzMikvwMCwMIYt6B6RUY3Ib9uo4bj6O59GSXolR41ieUDwJQccT6sqrsm+aMk53f3jVV3PFhxvXuHL38Y4+mnn34kMQML7uyzzsz+paWJLe+mAweSHD+x5bFtTfQ7IAAsknELqvdKclVVXZrkU4cmdvfjphIVAMynI8qHVXVMRsXUl3T3K4fJY907fPnDGPfu3essIeAz7F9ayrUXTq4AesL5N05sWWxrvgMCwBrGLag+e5pBAMA28ezDfUGNTkV9QZKru/uXl816ddw7HID59exZBwAA82qsgmp3v7GqPi/J/bv7r6vqLkmOnm5oADBfjjAfPizJU5K8u6r2DdOemVEh1b3DAZhLk/4OWFU/kuS7Mnqw1buTPK27PzmZaAFga41VUK2q787o/m33yOhJj3uS/Fbc8xSAHeRI8mF3vzlJrTFbHgVgLk3yO2BV7UnyXzN6yNXNVfXyJE9McvHEAgaALXTUmO1+IKMzbG5Mku5+b5LPmVZQADCn5EMAdopJ57xdST67qnYluUuSazcdIQDMyLgF1U919y2HRoYk6MEYAOw08iEAO8XEcl53LyX5pST/nOS6JB/r7r+aSJQAMAPjPpTqjVX1zIyOKH5dku9P8qfTCwsA5pJ8CMBOMbGcV1UnJTknyX2SfDTJH1bVt3f3761od15GtxnI6aefvonQ2Qpnn3Vm9i8t3T5+6y2fzDHHHnf7+Gl79uTSfVet+5rV2sBOd/NNB3Lq7hNvH7efzKdxC6rPSPL0jG4e/j1JXpPkd6YVFADMKfkQgJ1ikjnva5N8oLtvSJKqemWSr0xyp4Jqd1+U5KIk2bt3rytA5tz+paVce+Hxt4+fcP6NueG597x9/NQLljZ8zWptYKe77eBB+8k2MFZBtbsPJvnt4QcAdiT5EICdYsI575+TPLSq7pLk5owebHXZBJYLADMxVkG1qj6QVe6X0933nXhEMAErT5GHQ246cCDJ8Ru2g9XIhwDsFJPMed39tqp6RZK3J7ktyTsynIkKANvRuJf87102fFyS/5zkHpMPByZj5SnycMgJ59846xDY3uRDAHaKiea87n5WkmdtNigAmAdHjdOou/9t2c9Sd/9Kkm+cbmgAMF/kQwB2CjkPANY27iX/X7ps9KiMjlaOe3YrACwE+RCAnULOA4C1jZsQ/+ey4duSXJPkWyceDQDMN/kQgJ1CzgOANYxVUO3uR0w7EACYd/IhADuFnAcAaxv3kv8fXW9+d//yZMIBgPklHwKwU8h5ALC2cS/535vkIUlePYw/NsmlSd47jaAAYE7JhwDsFHIeAKxh3ILqaUm+tLs/niRV9ewkf97d3z6twABgDsmHAOwUch4ArOGoMdudnOSWZeO3DNMAYCeRDwHYKeQ8AFjDuGeovijJpVX1x8P445NcMpWIAGB+yYcA7BRyHgCsYayCanf/fFX9RZKvGiY9rbvfMb2wAGD+yIcA7BRyHgCsbdxL/pPkLklu7O7nJ9lfVfeZUkwAMM/kQwB2CjkPAFYxVkG1qp6V5CeS/OQw6ZgkvzetoABgHsmHAOwUch4ArG3cM1S/OcnjktyUJN19bZITphUUAMwp+RCAnULOA4A1jFtQvaW7O0knSVUdP72QAGBuyYcA7BRyHgCsYdyC6sur6n8nuXtVfXeSv07y29MLCwDmknwIwE4h5wHAGnZt1KCqKsnLkjwwyY1JHpDkv3f3a6ccGwDMDfkQgJ1CziNJzj7rzOxfWrp9/LQ9e3Lpvqsmuo6bbzqQU3efePv4rbd8Mscce9xU1wmHayv2BbafDQuq3d1V9Zru/uIkEigAO5J8CMBOIeeRJPuXlnLthXfc6eHUC5bWaX1kbjt48E7rOOH8G3PDc+851XXC4dqKfYHtZ9xL/t9eVQ+ZaiQAMP/kQwB2CjkPANaw4Rmqgy9P8u1VdU1GT3msjA5cfsm0AgOAOSQfArBTyHkAsIZ1C6pVdXp3/3OSr9+ieABg7siHAOwUch4AbGyjM1RfleRLu/uDVfVH3f0tWxATAMybV0U+BGBneFXkPABY10b3UK1lw/edZiAAMMfkQwB2CjkPADawUUG11xgGgJ1EPgRgp5DzAGADG13y/6CqujGjo5SfPQwnd9yQ/G5TjQ4A5oN8CMBOIecBwAbWLah299FbFQgAzCv5EICdQs4DgI1tdMk/AAAAAAADBVUAmKKqemFVXV9VVyyb9uyqWqqqfcPPY2YZIwAAAONTUAWA6bo4yaNXmf687j5r+HnNFscEAADAEVJQBYAp6u43Jfn3WccBAADAZCioAsBs/GBVvWu4JcBJsw4GAACA8eyadQAAsAP9ZpILk/Tw+38m+c7VGlbVeUnOS5LTTz99q+KbC2efdWb2Ly1NdJk3HTiQ5PiJLhPYejffdCCn7j5xYss7bc+eXLrvqoktDwBYbAqqALDFuvvDh4ar6reT/Nk6bS9KclGS7N27t6cf3fzYv7SUay+cbPHzhPNvnOjygNm47eDBif59OPWCyR684TNV1d2T/E6SL8rogOJ3dvdbZhoUABwhl/wDwBarqlOWjX5zkitmFQsAbJHnJ/nL7n5gkgcluXrG8QDAEXOGKgBMUVW9NMnDk9yrqvYneVaSh1fVWRmdoXNNku+ZVXwAMG1VdWKSr07y1CTp7luS3DLLmABgMxRUAWCKuvtJq0x+wZYHAgCzc58kNyT53ap6UJLLk/xwd98027AA4MgoqAIAADBNu5J8aZIf6u63VdXzkzwjyQXLG+3kBzFuBysfBrfRgx5Xe3jctB8OufKBlh44x2o2+zk53H2BxaSgCgAAwDTtT7K/u982jL8io4LqnezkBzFuBysfBrfRgx5Xe3jctB8OufKBlh44x2o2+zk53H2BxTSzh1JV1dFV9Y6q+rNh/D5V9baqel9Vvayqjp1VbAAAAExGd38oyb9U1QOGSY9K4rRBALatWZ6h+sMZPdnxbsP4c5M8r7v/oKp+K8nTk/zmrIIDANjuVrvccrNcPgkcoR9K8pLhxJn3J3najOMBgCM2k4JqVZ2W5BuT/HySH62qSvLIJP9laHJJkmdHQRUA4IitdrnlZrl8EjgS3b0vyd5ZxwEAkzCrS/5/JcmPJzk4jN8zyUe7+7ZhfH+SPTOICwAAAABgTVteUK2qb0pyfXdffoSvP6+qLquqyw4edJ9yAAAAAGDrzOKS/4cleVxVPSbJcRndQ/X5Se5eVbuGs1RPS7Lq9WTLn/x47DFHq6gCAAAAAFtmy89Q7e6f7O7TuvuMJE9M8jfd/eQkr0/yhKHZuUn+ZKtjAwAAAABYz6zuobqan8joAVXvy+ieqi+YcTwAAAAAAHcyi0v+b9fdb0jyhmH4/UnOnmU8AAAAAADrmaczVAEAAAAA5pqCKgAAAADAmBRUAQAAAADGpKAKAAAAADAmBVUAAAAAgDEpqAIAAAAAjElBFQAAAABgTAqqAAAAAABjUlAFAAAAABjTrlkHAAAAMEs333Qgp+4+cWLLO23Pnly676qJLY+d6eyzzsz+paXbxzf7uZr08ubByj7ddOBAkuNnFxBTd7if45Xtk+l/TlbmlEnva4u4L29HCqoAAMCOdtvBg7n2wsl9uT71gqWNG8EG9i8t3elzudnP1aSXNw9W9umE82+cYTRshcP9HK9sn0z/c7Iyp0x6X1vEfXk7csk/AAAAAMCYFFQBAAAAAMakoAoAAAAAMCYFVQAAAACAMSmoAgAAAACMSUEVAAAAAGBMCqoAAAAAAGNSUAUAAAAAGJOCKgAAAADAmBRUAQAAAADGpKAKAAAAADAmBVUAmKKqemFVXV9VVyybdo+qem1VvXf4fdIsYwQAAGB8CqoAMF0XJ3n0imnPSPK67r5/ktcN4wAAAGwDCqoAMEXd/aYk/75i8jlJLhmGL0ny+K2MCQAAgCOnoAoAW+/k7r5uGP5QkpNnGQwAAADjU1AFgBnq7k7Sa82vqvOq6rKquuyGG27YwsgAYLKq6uiqekdV/dmsYwGAzVBQBYCt9+GqOiVJht/Xr9Wwuy/q7r3dvXf37t1bFiAATMEPJ7l61kEAwGYpqALA1nt1knOH4XOT/MkMYwGAqauq05J8Y5LfmXUsALBZu2YdAAAssqp6aZKHJ7lXVe1P8qwkz0ny8qp6epIPJvnW2UUIAFviV5L8eJIT1mpQVeclOS9JTj/99K2JaoGcfdaZ2b+0dPv4rbd8Mscce9zt4zcdOJDk+NvHb77pQE7dfeLt46ft2ZNL9121JbGOa2WMG/UJVu4Hi/AZWbkfTLtPK9/DefzbMA8UVAFgirr7SWvMetSWBgIAM1JV35Tk+u6+vKoevla77r4oyUVJsnfv3jXvL87q9i8t5doL7yiynHD+jbnhufe80/hytx08eKf2p16wlHmzMsaN+gSr7Qfb3Wr7wTStfA/n8W/DPHDJPwAAANP0sCSPq6prkvxBkkdW1e/NNiQAOHIKqgAAAExNd/9kd5/W3WckeWKSv+nub59xWABwxBRUAQAAAADG5B6qAACMbeWDETbLgw5gZ+nuNyR5w4zDAIBNUVAFAGBsKx+MsFkedAAAwHbjkn8AAAAAgDEpqAIAAAAAjElBFQAAAABgTAqqAAAAAABjUlAFAAAAABiTgioAAAAAwJgUVAEAAAAAxqSgCgAAAAAwJgVVAAAAAIAxKagCAAAAAIxJQRUAAAAAYEwKqgAAAAAAY1JQBQAAAAAY066tXmFV3TvJi5KcnKSTXNTdz6+qeyR5WZIzklyT5Fu7+yNbHR8AAADMm5tvOpBTd594+/hpe/bk0n1XTW35Nx04kOT4iS1/Fqb9nq109llnZv/S0sTWt3J5k1jmdrPyPZjG53LWn5PN9mmz8W/Fe7yItrygmuS2JD/W3W+vqhOSXF5Vr03y1CSv6+7nVNUzkjwjyU/MID4AAACYK7cdPJhrL7yjyHHqBUvrtN788k84/8aJLn8Wpv2erbR/aWmi61u5vEksc7tZ+R5M43M568/JZvu02fi34j1eRFt+yX93X9fdbx+GP57k6iR7kpyT5JKh2SVJHr/VsQEAAAAArGcWZ6jerqrOSPLgJG9LcnJ3XzfM+lBGtwRY7TXnJTkvSY4+qrYgSgAAAACAkZk9lKqq7prkj5Kc3913Op+4uzuj+6t+hu6+qLv3dvfeoxRUAQAAAIAtNJOCalUdk1Ex9SXd/cph8oer6pRh/ilJrp9FbAAAAAAAa9nygmpVVZIXJLm6u3952axXJzl3GD43yZ9sdWwAAAAAAOuZxT1UH5bkKUneXVX7hmnPTPKcJC+vqqcn+WCSb51BbAAAAAAAa9rygmp3vznJWjc/fdRWxgIAAAAAcDhm9lAqAAAAAIDtRkEVAAAAAGBMs7iHKgCwgM4+68zsX1qa2PJuOnAgyfETWx4AAMAkKKgCABOxf2kp1144uQLoCeffOLFlAQAATIpL/gEAAAAAxqSgCgAAAAAwJpf8A8CMVNU1ST6e5NNJbuvuvbONCAAAgI0oqALAbD2iu/911kEAAAAwHpf8AwAAMDVVde+qen1VXVVVV1bVD886JgDYDAVVAJidTvJXVXV5VZ0362AAYEpuS/Jj3X1mkocm+YGqOnPGMQHAEXPJPwDMzn/s7qWq+pwkr62q93T3m5Y3GAqt5yXJ6aefPosYYapuvulATt194sSWd9qePbl031UTW97ZZ52Z/UtLE1tektx6yydzzLHHTWx5Nx04kOT4iS0PJq27r0ty3TD88aq6OsmeJJPbWQFgCymoAsCMdPfS8Pv6qvrjJGcnedOKNhcluShJ9u7d21seJEzZbQcP5toLJ1cMPPWCyRY/9y8tTTS+JDnh/Btzw3PvOdHlwXZRVWckeXCSt804FAA4YgqqADADVXV8kqOGM3WOT/KfkvzsjMMCgKmpqrsm+aMk53f3ZxwJ2O5XZaw8o32jM+YPt/1KK8/wd7b6xla+ZyuvGDjcbbby9Rttg81u83mw0XuwHfu00qT3ra3eV1eub+U2WbkNN4pntauJFmE7b5aCKgDMxslJ/riqklE+/v3u/svZhgQA01FVx2RUTH1Jd79ytTbb/aqMlWe0b3TG/OG2X2nlGf7OVt/Yau/Z8isGDnebrXz9Rttgs9t8Hmz0HmzHPq006X1rq/fVletbuU1W24aHs7zVlrkTKagCwAx09/uTPGjWcQDAtNXo6OELklzd3b8863gAYLOOmnUAAAAALLSHJXlKkkdW1b7h5zGzDgoAjpQzVAEAAJia7n5zkpp1HAAwKc5QBQAAAAAYk4IqAAAAAMCYFFQBAAAAAMakoAoAAAAAMCYFVQAAAACAMSmoAgAAAACMSUEVAAAAAGBMCqoAAAAAAGNSUAUAAAAAGJOCKgAAAADAmHbNOgAAAJiUm286kFN3nzix5d104ECS4ye2PAAAtj8FVQAAFsZtBw/m2gsnVwA94fwbJ7YsAAAWg0v+AQAAAADGpKAKAAAAADAmBVUAAAAAgDG5hyoAAAAL5eyzzsz+paXbx0/bsyeX7rvqiF+fJLfe8skcc+xxa45v9BC7lctc2X7lQ/U8FG/rrdwGh7uNN1reVmzTlZ+zlX1YuS8cbvvNxrPR+je7Pj7TND6HK5d5uNv1cLf7PH5OFFQBAABYKPuXlu70gLpTL1hap/XGr09GD6m74bn3XHf8cJa5sv3Kh+p5KN7WW20bHM42Hmd507ba52x5H1buC4fbfrPxbLT+za6PzzSNz+HKZR7udj3c7T6PnxOX/AMAAAAAjMkZqgCwQ612OeNmuDQRYGTlpZCbNQ+XNgIAd1BQBYAdarXLGTfDpYkAIysvhdysebi0EQC4g0v+AQAAAADGpKAKAAAAADAmBVUAAAAAgDEpqAIAAAAAjElBFQAAAABgTAqqAAAAAABj2jXrAACA8Vx91ZU5dfeJE1veTQcOJDl+YssDYDpuvunARP/+AwCbo6AKANvErbfekmsvPGliyzvh/BsntiwApue2gwdz7YWTPQBW3ycHAMCRcsk/AAAAAMCYFFQBAAAAAMakoAoAAAAAMKa5KqhW1aOr6h+q6n1V9YxZxwMA0yTvAbBTyHkALJK5KahW1dFJ/leSb0hyZpInVdWZs40KAKZD3gNgp5DzAFg0c1NQTXJ2kvd19/u7+5Ykf5DknBnHBADTIu8BsFPIeQAslHkqqO5J8i/LxvcP0wBgEcl7AOwUch4AC6W6e9YxJEmq6glJHt3d3zWMPyXJl3f3D65od16S84bRL0pyxZYGOj33SvKvsw5iAhalH4m+zKNF6UeyOH1ZlH4kyQO6+4StWtkOz3uL9LlZlL4sSj8SfZlHi9KPZLH6smV5b4fnvGRxPjeL0o9EX+bRovQjWZy+LEo/kinkvF2TXNgmLSW597Lx04Zpd9LdFyW5KEmq6rLu3rs14U3XovRlUfqR6Ms8WpR+JIvTl0XpRzLqyxavcsfmvUXpR7I4fVmUfiT6Mo8WpR/J4vVlC1e3Y3Nesjh9WZR+JPoyjxalH8ni9GVR+pFMJ+fN0yX/f5/k/lV1n6o6NskTk7x6xjEBwLTIewDsFHIeAAtlbs5Q7e7bquoHk/yfJEcneWF3XznjsABgKuQ9AHYKOQ+ARTM3BdUk6e7XJHnNYbzkomnFMgOL0pdF6UeiL/NoUfqRLE5fFqUfyQz6soPz3qL0I1mcvixKPxJ9mUeL0o9EX47YDs55yeL0ZVH6kejLPFqUfiSL05dF6Ucyhb7MzUOpAAAAAADm3TzdQxUAAAAAYK7NVUG1qh5dVf9QVe+rqmesMv+zquplw/y3VdUZw/Qzqurmqto3/PzWstd8WVW9e3jNr1ZVzXE/nrysD/uq6mBVnTXMe8OwzEPzPmfa/RizL19dVW+vqtuq6gkr5p1bVe8dfs5dNn0et8mq/aiqs6rqLVV1ZVW9q6q+bdm8i6vqA8u2yVnT7sdm+jLM+/SyeF+9bPp9hs/i+4bP5rHz3JeqesSKfeWTVfX4Yd6Wb5cx+vGjVXXV8Bl6XVV93rJ5c7OfbKYv87avbHKbbMl+MkaM2yLnbbIvc5X3jvRv0jBvu+3L2yLvbXKbyHmz68u2yHtH2o95208205dhnry3Nf2Yq5w3Zl+2Rd470n5s031Z3pu//0W2Rc7bTF/mbV/Z5DaZ3H7S3XPxk9HNyf8pyX2THJvknUnOXNHm+5P81jD8xCQvG4bPSHLFGsu9NMlDk1SSv0jyDfPajxVtvjjJPy0bf0OSvXO4Tc5I8iVJXpTkCcum3yPJ+4ffJw3DJ83xNlmrH1+Q5P7D8KlJrkty92H84uVt532bDPMOrLHclyd54jD8W0m+b977suKz9u9J7jKL7TJmPx6xLL7vyx1/u+ZmP5lAX+ZmX9lMP4bxqe8nY8Y49zlvs31Z0WameW/MfpyROc95E+jLdtuXV+3HME/Om11f5j7vbbIfc7OfbLYvw7i8twX9WNHGd7356Md23JdX7cswT96bTT/mPudNoC9zs69sph/D+MT2k3k6Q/XsJO/r7vd39y1J/iDJOSvanJPkkmH4FUketV4lv6pOSXK37n5rj96VFyV5/MQjv7NJ9eNJw2tnacO+dPc13f2uJAdXvPbrk7y2u/+9uz+S5LVJHj2v22StfnT3P3b3e4fha5Ncn2T3lONdz2a2yaqGz94jM/osJqPP5uMnFvHaJtWXJyT5i+7+xPRCXdc4/Xj9svjemuS0YXie9pNN9WXO9pXNbJNVTWE/WZSclyxO3luUnJcsTt6T8z7TrHNesjh5b1FyXiLvzdXnJtsj5yWLk/cWJecl8t5qZp33FiXnbaovc7avzE3Om6eC6p4k/7JsfP8wbdU23X1bko8luecw7z5V9Y6qemNVfdWy9vs3WOakbbYfh3xbkpeumPa7w2nJF2zRKeHj9OVwXzuv22RDVXV2RkdA/mnZ5J8fTiN/XlV91ubCHMtm+3JcVV1WVW89dNlERp+9jw6fxSNZ5pGayHbJ6Mj/yn1lK7fL4fbj6RkdhVzvtbPYT9aLZy3L+3K7OdhXNtuPrdhPFiXn3SnOdda7HfLeouS89eI5LNtwX15JzpuORcl7i5LzEnlvvWVO2qLkvGRx8t6i5LxE3lvNrPPeouS89eJZy7zmvbnJefNUUN2M65Kc3t0PTvKjSX6/qu4245iOWFV9eZJPdPcVyyY/ubu/OMlXDT9PmUlwO9RwFOnFSZ7W3YeOoP1kkgcmeUhGp/H/xIzCOxyf1917k/yXJL9SVfebdUCbMWyXL07yf5ZNntvtUlXfnmRvkl+cdSybtVZfttu+skY/5n0/Waicl8h782i77ctrmPd9+bBst5yXLE7eW5Scl8h780DOmz/bcV9ew7zvy4dlu+W9Rcl5yeLkvWnnvHkqqC4lufey8dOGaau2qapdSU5M8m/d/anu/rck6e7LM6qUf8HQfvmpvastc9KOuB/L5n/GUZjuXhp+fzzJ72d0mvO0jdOXw33tvG6TNQ3/sP15kp/q7rcemt7d1/XIp5L8buZ/myz/HL0/o3s1PTijz97dh8/iYS9zEzbVl8G3Jvnj7r710IQZbJex+lFVX5vkp5I8bohtvdfOYj9ZL547WaMv87SvbKofW7SfLErOu1Oc66x3O+S9Rcl568Uzlu22L69FzpuaRcl7i5LzEnlvvWVO2qLkvDvFOdiueW9Rcl4i7600D3lvUXLeevHcyTbIe/OT83qLbhy70U+SXRndpPc+uePGsl+4os0P5M43+H75MLw7ydHD8H2Hjt+jV7/Z72PmtR/D+FFD/Pddscx7DcPHZHRfh++dh22yrO3F+cwblX8go5svnzQMz+02WacfxyZ5XZLzV2l7yvC7kvxKkufM+TY5KclnDcP3SvLeDDdvTvKHufMNmL9/nvuybPpbkzxilttlzH3+wRn983//FdPnZj+ZQF/mZl/ZZD+2ZD8ZM8a5z3mb7cswPhd5b5x+LGt7ceY0502gL9tqX16nH3LeDPuSbZD3NtmPudlPJtAXeW+L+jGMz0XOG7cvy9penDnNe5vsx7bbl9fpi7w3o35kG+S8CfRlbvaVTfZjovvJVDfYEbwxj0nyj0PHf2qY9rMZVZST5Lihk+8bPoD3HaZ/S5Irk+xL8vYkj122zL1JrhiW+etJal77Mcx7eJK3rlje8UkuT/KuoZ/Pz/BPxRz05SEZ3V/ipoyq+lcue+13Dn18X0anhM/zNlm1H0m+Pcmtw2fr0M9Zw7y/SfLuoS+/l+Su87xNknzlEO87h99PX7bM+w6fxfcNn83Pmue+DPPOyOgf0qNWLHPLt8sY/fjrJB9e9hl69TzuJ5vpy7ztK5vox5btJ2PEuC1y3mb6Msx7eOYk743Rj22R8zbTl2y/fVnOm8//RbZF3jvSfszbfrLJvsh7W9SPYd7DMyc5b8y+bIu8d6T9yPbcl+W9+ftfZFvkvM30Zd72lU30Y6L7SQ0vBAAAAABgA/N0D1UAAAAAgLmmoAoAAAAAMCYFVQAAAACAMSmoAgAAAACMSUEVAAAAAGBMCqpwBKrq01W1r6quqKo/rKq7TGk9e6vqV4fhh1fVVx7BMs6vqu8Yhh84xP2OqrrfJmM7q6oes2z8cVX1jCNc1u6q+svNxAPA9Mh78h7ATiHnyXkwDgVVODI3d/dZ3f1FSW5J8r3jvKiqdh3OSrr7su7+r8Pow5McVpId1vedSX5/mPT4JK/o7gd39z8ta1dVdbh/D85KcnuS7e5Xd/dzDnMZh157Q5LrquphR/J6AKZO3pP3AHYKOU/Ogw0pqMLm/W2Sz6+qe1TVq6rqXVX11qr6kiSpqmdX1Yur6u+SvLiqzqiqvxnava6qTh/a/efhKOg7q+pNw7SHV9WfVdUZGSXyHxmOOn5VVX2gqo4Z2t1t+fgyj0zy9u6+bTjCeH6S76uq1w9x/ENVvSjJFUnuXVW/WVWXVdWVVfUzhxZSVQ+pqv87xHZpVZ2Y5GeTfNsQz7dV1VOr6teH9mv18eKq+tVhWe+vqicsi/VVSZ48we0CwHTIe/IewE4h58l5sCoFVdiEGh0V/IYk707yM0ne0d1fkuSZSV60rOmZSb62u5+U5NeSXDK0e0mSXx3a/PckX9/dD0ryuOXr6e5rkvxWkucNR0v/Nskbknzj0OSJSV7Z3beuCPFhSS4flvGaZct4xDD//kl+o7u/sLs/mOSnuntvki9J8jVV9SVVdWySlyX54SG2r01y0xDvy4Z4XrZivWv1MUlOSfIfk3xTkuVHOS9L8lUBYG7Je/IewE4h58l5sB4FVTgyn11V+zJKDP+c5AUZJY4XJ0l3/02Se1bV3Yb2r+7um4fhr8gdl2W8eHhdkvxdkour6ruTHD1GDL+T5GnD8NOS/O4qbU5JcsM6y/hgd7912fi3VtXbk7wjyRdm9M/BA5Jc191/P/Ttxu6+bYPY1upjkryquw9291VJTl42/fokp26wXABmQ95bn7wHsDjkvPXJeZDksO7xAdzu5u4+a/mEqlqv/U0bLbC7v7eqvjyjI5GXV9WXbdD+74bLLR6e5OjuvmK1OJMcN05cVXWfJP8tyUO6+yNVdfEGrz1Sn1o2vPxNOy6jeAGYP/LekZP3ALYXOe/IyXnsGM5Qhcn52wz3hRkS3792942rtPu/GV22kaH93w6vuV93v627/3tGRxrvveJ1H09ywoppL8ro6OBqRyyT5Ooknz9m/HfLKOl+rKpOzujyliT5hySnVNVDhjhPGC5/WS2eQ1bt4wa+IKP7+wCwPch7d5D3ABabnHcHOQ+ioAqT9OwkX1ZV78rofjHnrtHuh5I8bWj3lCQ/PEz/xap6d1VdkVGSeueK1/1pkm8+dKPyYdpLkpyU5KVrrOsvknz1OMF39zszuvzjPRkl7r8bpt+S5NuS/FpVvTPJazM6wvj6JGceulH5mH1czyOS/Pk4sQIwF54deW+jPq5H3gPYPp4dOW+jPq5HzmPhVHfPOgbgCA1PTjynu5+yTps/TvLj3f3erYvs8NXoaZfndPdHZh0LAPNJ3gNgp5DzYL4pqMI2VVW/ltGlGo/p7n9cp90Dkpzc3W/asuAOU1XtTvKw7n7VrGMBYD7JewDsFHIezD8FVQAAAACAMbmHKgAAAADAmBRUAQAAAADGpKAKAAAAADAmBVUAAAAAgDEpqAIAAAAAjElBFQAAAABgTP8/+RG6dHyL21QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nbin1 = 3; nbin2 = 20; nbin3 = 100\n",
    "\n",
    "plt.subplot(131)\n",
    "GSLIB.hist_st(df['Porosity'].values,pormin,pormax,log=False,cumul = False,bins=nbin1,weights = None,xlabel='Porosity (fraction)',title='Histogram with ' + str(nbin1) + ' Bins')\n",
    "\n",
    "plt.subplot(132)\n",
    "GSLIB.hist_st(df['Porosity'].values,pormin,pormax,log=False,cumul = False,bins=nbin2,weights = None,xlabel='Porosity (fraction)',title='Histogram with ' + str(nbin2) + ' Bins')\n",
    "\n",
    "plt.subplot(133)\n",
    "GSLIB.hist_st(df['Porosity'].values,pormin,pormax,log=False,cumul = False,bins=nbin3,weights = None,xlabel='Porosity (fraction)',title='Histogram with ' + str(nbin3) + ' Bins')\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=3.0, top=1.1, wspace=0.1, hspace=0.2); plt.show()\n",
    "#plt.savefig('hist_Porosity_Multiple_bins.tif',dpi=600,bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See what happens when we use:\n",
    "\n",
    "* **too large bins / too few bins** - often smooth out, removes information\n",
    "* **too small bins / too many bins** - often too noisy, obscures information  \n",
    "\n",
    "#### Plotting a Histogram with the matplotlib Package\n",
    "\n",
    "I don't want to suggest that matplotlib is hard to use. The GSLIB visualizations provide convenience and once again use the same parameters as the GSLIB methods. Particularly, the 'hist' function is pretty easy to use, just a lot more code to write.  \n",
    "\n",
    "* here's how we can make the same histogram as above with matplotlib directly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['Porosity'].values,alpha=0.8,color=\"darkorange\",edgecolor=\"black\",bins=20,range=[pormin,pormax])\n",
    "plt.title('Histogram'); plt.xlabel('Porosity (fraction)'); plt.ylabel(\"Frequency\")\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=1.0, wspace=0.1, hspace=0.2); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can demonstrate normalized histograms with matplotlib. \n",
    "\n",
    "* I didn't add this functionality to GeostatsPy's hist function\n",
    "\n",
    "#### Normalized Histograms\n",
    "\n",
    "Normalized histograms are convienient since we can read propability to be in each bin and observe closure by summing the probability for all bins is 1.0.\n",
    "\n",
    "* to do this we need to explicity set the weight for each data as $\\frac{1}{n}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weights = np.ones(len(df)) / len(df)\n",
    "plt.hist(df['Porosity'].values,alpha=0.8,color=\"darkorange\",edgecolor=\"black\",bins=25,range=[pormin,pormax],weights=weights)\n",
    "plt.title('Normalized Histogram'); plt.xlabel('Porosity (fraction)'); plt.ylabel(\"Prob\")\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=1.0, wspace=0.1, hspace=0.2); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Probability Density Functions\n",
    "\n",
    "The practical way to calculate a probability density function (PDF) from data is to use of kernel density estimate (KDE).\n",
    "\n",
    "* we place a kernel, in this case a parametric Gaussian PDF, at each data value and then calculate the sum of all data kernels.\n",
    "* constrained for closure such that the area under the curve is 1.0.\n",
    "* differentiating the data CDF is usually too noisy to be useful.\n",
    "\n",
    "To demonstrate the KDE method, we calculate the KDE PDF for the first 2, 5, ..., 200 data. \n",
    "\n",
    "* when there are very few data you can see the individual Gaussian kernels\n",
    "* with more data they start to smooth out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nums=[2,4,10,20,50,200]\n",
    "\n",
    "for i, num in enumerate(nums):\n",
    "    plt.subplot(2,3,i+1)\n",
    "    sns.kdeplot(x=df['Porosity'].values[:num],color = 'darkorange',alpha = 1.0,linewidth=3,bw_method=0.1,)\n",
    "    plt.xlim([0,0.25])\n",
    "    plt.title('KDE PDF for First ' + str(num) + ' Data'); plt.xlabel('Porosity (fraction)'); plt.ylabel(\"Density\")\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=3.0, top=2.1, wspace=0.1, hspace=0.2); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use the Seaborn package to calculate and plot the PDF from our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Wwt3y8vIxc9rYTu0r9xALIYwmCVj4ryaL3EMshPBb0gUthBBCGEASsBBCCGEAScBCCCGEASQBC79jtTI25pajur4JReX1RocjhBCdIoOwhF85UlKLv3y6D5vzK1BU2YAL/rYe/bpH4Q+X9sfYfvFGhyeEEC6TFrDwG7/sKcOcF7Zgc36Fpj7veA3unZ+NbfsrDYpMCCE6ThKw8AulVRY8viAHtZYmh49bGq148O2dyD9W7eXIhBCicyQBC7/w/Fd5qKxtbHObytpGPPZBDqxW9lJUQgjReZKAhc9btbMEy7ed0NTdenYfZCSH485z0jX1ecdrsGxrsTfDE0KITpEELHya1cp48dv9mrohvWNwx4y+ICLcNj0dM0Ylax7/7/KDaGqSVrAQwrdJAhY+bUNuOQ4U17aUTQT8+YoBMJsJAEBEuOe8DJiodZ+DJ2qxeHORt0MVQogOkQQsfNqna45qyjNGpWBgWoymrk9yJGaO766pe3vlITBLK1gI4bskAQufdbS0Dqt3l2jqrpyc5nDb26anI8SmGXzwRC12HKjyaHxCCNEVkoCFz/p83VHYDmgemBaNkRmxDrftmRSBM4Z109R9s/G4J8MTQogukQQsfJLVyvh2k/Y67hWn9gQROdkDuHBcqqa8fPsJWBqsHolPCCG6ShKw8Ek7D1WhuNLSUo4MM+O8Malt7AFMHpyIhOjQlnJVbSNW7y71WIxCCNEVkoCFT/ohW3vt97TBiYgMN7e5T4jZhHNHp2jqvt0k3dBCCN8kCVj4HGbGyh3aiTfOGpHsZGstfTf0L3vKUFPvePpKIYQwkiRg4XNyj1bjcEldSznUTDhtcKJL+w7pHYPe3SJayg1NjPX7ytweoxBCdJXHEjARvUVERUSUbVP3PBHlENF2IvqSiBI8dX7hv/TdzxMHJCI6wrWVM4kIU4ckaepW75LrwEII3+PJFvA7AM7T1S0HMJyZRwLYC+AxD55f+Kkfd2oT8FkjujnZ0rEpugT8c06ZLNAgvG7/8Ro8+3kurvn3Zkx+bA2u+fdm5Bw+aXRYwod4LAEz8yoApbq675i5eUmbdQB6e+r8wj+VVlmwt7B1SUECMHVokvMdHBiTGY8omwFbJVUW7CmUDz7hPcfK6nDrq9vw+bqj2FtYDUujFXsLq3Hbf7bhx+wT7R9ABAUjrwHfAmCJgecXPmhDbrmmPLh3DBJjwjp0jLBQEyYNTNDUSTe08BarlfHEx3sdLp9Z12DF797djR92SBIWBiVgInocQCOABW1scwcRbSSijcXFsrxcsPh1X7mmPHFAQqeOM2Wwrhta7gcWXrJg1RFsyq9w+jgDeObzXFTWNHgvKOGTvJ6AiegmABcBuJbbmC2fmecx83hmHp+SkuJsMxFAmBnr9mpHLE8c4NroZz39deDdh0/KB57wuJIqC15bdkBTl54ciXvO66tZsausugEvfrMfIrh5NQET0XkAfg9gFjPXePPcwvcdLK5FUUXr7FfhISaMyojr1LGSYsMwoGd0S5kBbMpz3ioRwh2+3VgES2Pr9KdxkSF4/c4RuOXsdNwxo69m2682HEdFlXwMBjNP3ob0EYBfAAwiosNEdCuAVwDEAlhORFuJ6HVPnV/4H33385isOISFdv4tOqF/gqasv74shDsxMxbpFgC55ew+SE0IBwDcOK03MlOjNI/vP1wsy2YGMU+Ogr6amXsycygz92bmN5m5PzP3YebR6s9dnjq/8D+/7i3XlDvb/dxsfL94TXmjtICFB2UfrML+otYWrdlEuGBs68xsoSEm/PHy/pp9qqprsXr1aq/FKHyLzIQlfILVytiyX5sgJ+pGMnfU2Kx4zXW3/OM1KKmyON9BiC5YtEHb+p0yOAlJsdoR/GOy4nGmbtnM119/HVarrNoVjCQBC59woLhWc9tGTIQZ/XtEt7FH+2IiQzCkt3b94I3SDS08oL6hCcu2au/WuPiU7g63vevcvrBdVHPv3r1YuXKlB6MTvkoSsPAJ2woqNeWRfeNgMjlf+9dVp+huY9qQK93Qwv025VWg2mbRj6SYUEx2Mn95/57RmDFKe2eHtIKDkyRg4RP0Cbizo5/1JvTXXwcud8txhbC1Tjd+4cxh3RBidv7xeuc56ZrLIwUFBVi6dKmHohO+ShKw8AmeSsAj+8YhxOaT7nBJHU5UynVg4V76+9cnDWp7AGHf1ChcOE7bRf3GG2+gsdF+9iwRuCQBC8OVVllw8ERtS9lEwLA+sW3s4bqIMDOG9I7R1OmTvRBdUW9pQP7x1tHPJrK/Bc6R22ekg6j1y+GRI0ewaNEiT4QofJQkYGG47Qe0CXFwrxhE2iym0FWjM7Wt6S1tTBMoREeVVWon0xieHofYyPaXz0xLikCPZO0lkvnz58NikR6aYCEJWBjOU93PzUZnaj/ktkoLWLhRWWW1pnzqoASX9+3TsxvCwlpvVTp+/DgWLlzopsiEr5MELAy3raBKU3Z3Ah7ZV9udvbfwJJqaZMSp6Dqr1YpyXQKeNND1CWTCw0Ixe/ZsTd2bb76Juro6t8QnfJskYGEoq9WK3Yc9m4ATY8KQkRLZek4GKqtr29hDCNfs27cPjY2ttx/FRoZ0ePzCTTfdhIiIiJZySUkJPvroI7fFKHyXJGBhqKrqOjQ0tc6Fm5YYgZT4cLefR98NXXlSErDouq1bt2rKYzPjO3z/elJSEubMmaOpe+uttyDLsAY+ScDCUPqWqLtbv830A7EkAQt30Cdg/fvMVTfccAPi4lr3ra2txcsvv9yV0IQfkAQsDKVPhKMy3HP7kd7oDPsELPdciq5gZrcl4Li4ONx1l3ZtmsWLF2PHjh2dDU/4AUnAwjDM7CABe6YF3KtbBLrZTIxvtVqxd+9ej5xLBIejR49quonDQkwY3CumjT3advnll6N/f+1qSc8//7xMURnAJAELwxw4cEAzgCU63Ix+XVyAwRkismsF61svQnTEtm3bNOXh6bEIDen8R6rZbMYjjzyiqdu1axe++eabTh9T+DZJwMIw+g8wdy3A4MyYLEnAwn3sup/d0Hszfvx4nHXWWZq6V155BSdPnuzysYXvkQQsDKNPwJ7qfm42OkM7EnrLli1gZidbC9E2d13/1XvggQc0k3OUlpbivffec8uxhW+RBCwMo/8A83QCHtAzGlE2U1yWlZXh0KFDHj2nCExVVVXIz89vKROAEX3d8/5NS0vDjTfeqKn7+OOPUVkpM7gFGknAwhBlZWU4ePBgS9lEyjU0TzKbCSN055BuaNEZO3fu1PSeZHWPcmn+Z1ddf/31SEhIaCnX1NTggw8+cNvxhW+QBCwMsX37dk15kJsXYHBG300oCVh0hv72oJFu7r2JiorCDTfcoKn7+OOPUVEhC4kEEvd9ZROiA+yu/7qp+649jq4DC9FR+gSs71lxVV5ePmZOG+vwsaYmKw7k5aHB5k6B0yeORHrPbkhNy8CbC77o1DmF75AELAzh7eu/zYanx8J2oPWhQ4dQWlqKpKQkr5xf+D9mRnZ2tqau05dPmixYdF8vpw+/9wPhpcX7W8ppsfVYeG8aLn6loHPnEz5FuqCF11ksFuzevVtT560EHBlutpssQbqhRUccOnRIMyDKbDYjIzXKI+e6ZGJ3hNncW1xYVocNueUeOZfwPknAwut2796NhoaGlnLPxHCkJrh/AQZn9Asz6LvDhWiLvvs5NjrCY/evx0WF4uwRyZq6L3895pFzCe+TBCy8ztv3/+rJQCzRFY4SsCddOrGHpvxjdgksDTKPeSCQBCy8zqjrv830Mxbl5OSgtlZWRxKu0V//jYuOdLKle4zJikN6cus5Gq2M4lK5JzgQeCwBE9FbRFRERNk2dUlEtJyI9qn/Jnrq/MI3MbPdLUjeTsBJsWGIiGidaaipqQk7d+70agzCP9XV1dkt4uHpFjAR4eJTumvqSiuqPXpO4R2ebAG/A+A8Xd2jAL5n5gEAvlfLIogcPHgQ5eXlLWWz2Yz+HlqAoS3xMdpWi3RDC1fs2rVLszpR7969ERrq+ZtJpg3XXgeuqKqR+aEDgMcSMDOvAlCqq74YwLvq7+8CuMRT5xe+SX/915MDWNoSJwlYdIK+92bkyJFeOW96SiT6prS+Z5kZa9eu9cq5hed4+xpwd2Y+qv5+DEB3ZxsS0R1EtJGINtquuSn8mz7R6ROht8TFaG8b2b59O5qampxsLYTC7vLJqFFeO/cZw7ppyqtWrfLauYVnGDYIi5WJVJ0uRcPM85h5PDOPT0lJ8WJkwpP0LWCjEnBkeKjdXLu5ubmGxCL8g6PxCyNGjPDa+acO0U4Ws3btWvnS6Oe8nYCPE1FPAFD/LfLy+YWBysvLceDAgZayyWTy+AhSZ4gIo0eP1tTJ/cCiLYcOHdKMX4iKikL//v29dv6RfeMQZ7PgQ2Vlpbxn/Zy3E/DXAJrX2boRwFdePr8wkL71MHDgQJjNxt0Jp0/Ach1YtEV//+/w4cNhMnnv/Ws2E6boWsHr1q3z2vmF+3nyNqSPAPwCYBARHSaiWwE8B2AGEe0DMF0tiyBhd/+vF6+fOaJPwFu2bNEsMSeELX1r05vdz80mDUzQlGUxEf/myVHQVzNzT2YOZebezPwmM5cw89nMPICZpzOzfpS0CGC+loAHDRqE8PDWKTCLi4tx9OjRNvYQwUzfAjbi/Ts2SzuNanZ2NiwWi9fjEO4hM2EJr6irq8OuXbs0dfoWqLeFhoZi2LBhmjrphhaOVFdXIy8vT1M3fPhwr8fRIzECPWzmTW9oaJBJZPyYJGDhFdu3b0djY+v8tb1790ZqaqqBESnkOrBwxc6dOzUTcGRmZiIuzrszuDXTt4KlG9p/SQIWXqH/kBg71vEi5N4mCVi4wsjbj/T0i4lIAvZfkoCFV2zatElT9pUEPGrUKM1I1vz8fFRUVBgYkfBFRk7AoadvAW/btk3uB/ZTkoCFx1ksFrsVZHwlAUdHR2PAgAGaOmkFC1tWq9VuAJaRLeC+KZGa+adramrsFogQ/kESsPC4nTt3akZq9ujRA2lpaQZGpKX/MrB582aDIhG+qKCgAFVVVS3l2NhYZGRkGBYPEclc5gFCErDwOH1C85XWb7MxY8ZoynJNTdhy1Pr15gQcjuhnkNu9e7dBkYiucOldRERfENGFRCQJW3SYrydg/UCsnJwc1NTUGBOM8Dn6CTi8tQJSW/RrEEsC9k+uJtT/ALgGwD4ieo6IBnkwJhFAGhsb7Qaw+FoCTkpKQt++fVvKVqvVLmYRvPQtYF9IwNFR4SBqXcazoKBAvjT6IZcSMDOvYOZrAYwFUABgBRGtJaKbiSjUkwEK/5aTk4Pa2tqWcrdu3dCnTx8DI3JM/6VAuqEFAJSVlWH//v0tZZPJZDd5ixFCzGbNl0Zmxp49ewyMSHRGSPubKIioG4DrAFwPYAuABQCmQFlU4UxPBCf8n6PuZ9tv7kbJy8vHzGmtSfd4SQVy97dOQ/nc03/G4k//a7dfaloG3lzwhVdiFMbbuHGjpjxw4EBER0cbFI3WkCFDUFBQ0FLevXu33XgG4dtcSsBE9CWAQQDeBzCTmZs/qT4hoo3O9xTBzmev/zZZsOi+Xi3Fo6XdMPPZkpZyWIgVn9/dE2Gh2k6imS8VeCtC4QPWr1+vKZ9yyikGRWJvyJAhWLJkSUtZP9Wr8H2uXgP+LzMPZeZnm5MvEYUDADOP91h0wq9ZrVafnQFLr2dSBLrHt86xa2m0Ytfhqjb2EMHg119/1ZR9KQEPHTpUU5aBWP7H1QT8Vwd1v7gzEBF49u7di+rq6pZyfHw8MjMzDYyobXZz7OZXGhSJ8AVHjhxBYWFhSzk0NNTwBURsDRw4UHM558CBA5q/N+H72kzARNSDiMYBiCSiMUQ0Vv05E0CUNwIU/stR97PR90+2ZUyWbo7d/TIlZTDTdz+PHDkSERERTrb2vqioKLsvtDk5OQZFIzqjvWvA5wK4CUBvAC/Y1FcB+KOHYhIBQj+AxdcHiIzJ1LaAtxZUwmplmEzGDxoT3rdhwwZNeeLEiQZF4tzgwYORn5/fUs7JycG4ceMMjEh0RJvNEWZ+l5mnAbiJmafZ/MxiZhkKKpxqamqyS8Djx/v2cIGM1EgkRLfeVVdT34S9hdKlF4ysVqtPD8BqNmiQdkqG3NxcgyIRndFmC5iIrmPmDwBkENFD+seZ+QUHuwmBnTt3aiYGSExMRP/+/Q2MqH1EhDGZcfghu3U09Jb9FRjcO8bAqIQRcnNzUV5e3lKOjo7GkCFDjAvICf1CIpKA/Ut7F+Sab3iLARDr4EcIh/SthwkTJvj09d9m+m5ouQ4cnPTv3/Hjx8NsNhsUjXP6L7V5eXmwWq0GRSM6qs0WMDO/of77F++EIwKF/vaNSZMmGRRJx9gNxMqvBDP7xOQhwnv8ofsZUKZRTUxMRFlZGQBl6c9Dhw5pZskSvsvVxRj+QURxRBRKRN8TUTERXefp4IR/qqmpsZtL2Vc/wPQG9oxBVHhrS6esugEHimvb2EMEmoaGBrsR/L78/tW3gqUb2n+42id4DjNXArgIylzQ/QH8zlNBCf+2ZcsWNDU1tZTT09PRo0cPAyNyndlMGNVX3wqWbuhgsmPHDtTV1bWUU1JSDF3/tz1yHdh/uZqAm7uqLwTwP2aWTyThlC/PHuQK+/uBZUKOYKK//WjChAk+fQlC3wLet2+fQZGIjnJ1MYZviCgHQC2Au4koBUBdO/uIILV27VpN2Rfvn2yLDMQKbvovkL7+/pUuaP/l6nKEjwKYDGA8MzcAqAZwsScDE/6psLBQs0KL2Wz2uxbwsD6xCDW3tniOltXjWJl83wwG1dXVyM7O1tRNmDDBoGhck5WVpWmhHzlyRNYG9hMduS9kMICriOgGALMBnNPZkxLRg0S0k4iyiegjIvKd+d1El+hbv6NGjfKZ5dtcFRZqwvB07V120g0dHDZv3qy5jScjIwOpqakGRtS+iIgIzRrbzKyZHUv4LldHQb8P4J9Q1v+doP50alojIuoF4D4orenhAMwA5nTmWML36BPwaaedZlAkXWPXDS0DsYKCv9x+pCfd0P7J1WvA4wEMZWZ243kjiagByqIOhe1sL/yAxWKxG8AyefJkg6LpmjFZ8cDKQy1laQEHB39OwCtXrmwpSwL2D652QWcDcMt9JMx8BEpr+iCAowAqmPk7dxxbGGvr1q2orW29ZzY1NdXnp590ZmTfWNiuwbC/qAZlJy3GBSQ8rqSkBHl5eS1lk8nkNwsbSAvYP7magJMB7CKiZUT0dfNPZ05IRIlQBnBlAkgDEO1oUg8iuoOINhLRxuLi4s6cSnjZzz//rCmfeuqpPn37RluiI0IwqJd2Duit0goOaPrW75AhQxAb6x8z7jq6Fcl9HZbCU1ztgn7SjeecDmA/MxcDABF9AWWE9Qe2GzHzPADzAGD8+PHyTvJxzIxVq1Zp6vz1+m+zMZnx2H34ZEtZ6YYONy4g4VH+sPygM71790Z4eDjq6+sBABUVFSgpKUFycrLBkYm2uHob0k9QZsAKVX/fAGBzmzs5dxDAJCKKIqV5dDaA3Z08lvARBQUFOHz4cEs5NDTUb+Z/dmZMpn5CDhmIFaiY2e7+X1+//ciWyWRCv379NHXSDe37XB0FfTuAzwC8oVb1ArCwMydk5l/VY20GsEONYV5njiV8x+rVqzXl8ePHIyoqyqBo3GO0LgHvOXISjTZTbIrAcejQIRw/frylHBYWhlGjRhkYUcfJjFj+x9VrwL8BcBqASgBg5n0AOn1zHDM/wcyDmXk4M1/PzPWdPZbwDT/99JOmfPrppxsUifskxoQhM7X1S4SVgaqTMiFHINJf/x0zZgzCwsIMiqZzZCCW/3E1Adczc8sQUCIKASDXZQUAoKyszG71o0BIwIB9N3TFSZlhKBD56+1HtiQB+x9XB2H9RER/hHLv7gwA9wBY5LmwhD+58pILsC9nZ0s5OioCt80536V9DxTkQ7mi4ZvGZMXji1+PtZQrT8rShIHGarXig/feRk11dUvd26/+HZ+986JL+/vKe1ifgPfv34+mpiaYzWYnewijuZqAHwVwK5RrtncCWAxgvqeCEv4lPz8P/bu3dtfdPr037jzXtQ+koffmeCostxiVoW0Bn6ypkw+1AJOTk4Oa6uqW93BcZAhW/CELJpNrt9D5yns4KSkJSUlJKC0tBaBMjHPw4EFkZmYaHJlwxtVR0FYog67uYebZzPxfN86KJfxYTU0Nyiq13bJnDOtmUDTu1zMxHEkxoS3lpiarZrIG4f/0tx+N7x/vcvL1NdIN7V/aTMCkeJKITgDYA2APERUT0VzvhCd83Zo1azST16clRmBQL/9afKEtRGS3MMOOHTsMikZ4gt361f0TjAnEDSQB+5f2WsAPQhn9PIGZk5g5CcBEAKcR0YMej074vB9++EFTPmtEN7+d/cqZEenabmhJwIHDYrFg69atmrpTBiQaE4wbSAL2L+0l4OsBXM3M+5srmDkfwHUAbvBkYML3WSwWu+knzxoReDPvjMyQFnCg2rZtGyyW1jm+eySEo0+y/66OKgnYv7SXgEOZ+YS+Up1GMtTB9iKIrFu3TrPwd3JsmF13bSAY0lu7MMOBAwdQWSnzQgcCu9uPBiT4dQ9OVlaWJv4jR45o/kaFb2kvAbe1/IssDRPkli9frilPG97NbwevtCUq3Iz+PbXXtbOzsw2KRriTXQL24+u/ABAREYE+ffpo6vLz8w2KRrSnvQQ8iogqHfxUARjhjQCFb6qtrcWPP/6oqTt7ZOB1Pzcb3kfbspcE7P+qqqqwe7d2GvoJAxKMCcaNpBvaf7SZgJnZzMxxDn5imVm6oIPYqlWrtGv/xodhbFa8gRF51oi+MhAr0GzatEkzgr9f9yh0i/Wv6ScdkQTsP1ydilIIjaVLl2rK54xKCcju52Yj+tq3gG0/vIX/0Xc/Txzov6OfbUkC9h+SgEWHVVRUYO3atZq688Z0em0Ov5CeHInYyNaJ46qqqnDw4EEDIxJdpU/AE/oHRg+OowQs8yb5JknAosO+//57NNksyxcZER5Qk284YjKR3XVg/QIUwn+UlJSgoKCgtYIoYC6h9O7dG+Hh4S3l8vLylukphW+RBCw6bMmSJZpyalKsX9+64SpH3dDCP23evFlTjo2KQHSEq1Pj+zaTyYR+/fpp6qQb2jdJAhYdcuzYMWzZskVTl5IU52TrwCIDsQLHpk2bNOX42EiDIvEMfTf0nj17DIpEtEUSsOiQ7777TlMePnw4IiP8f+SoK4b1idGU8/LyZJIDP6VvAcfHRBkUiWcMGjRIU5YE7JsCo89FuMWt116GosKCNrfZvGs/qmvqW8rWmhI01pbBF9ZD9bS4qFBERrReW7Nardi1axfGjx9vYFSio0pLSzWTU5hMJsRF+lcLOC8vHzOnjXX6eEVVDXL3tA4SPHIgD9vXKpeOUtMy8OaCLzweo2ifJGDRoqiwAIvuc55I845V46p/MaDeK2kiYMmjAzH1sdXeCtFwcTHaeYK3b98uCdjP6C+hDBo0COWHdxoUTSc1Wdr8W62ua8QZfz7WUiYwPr2zByLDzZj5UoEXAhSukC5o4bJlW4o15Qn9EwJi4oKOiI3WtpRkIJb/0V//HTvWeUvSX0VHhKBPt9Yviwxg39Fq4wISDkkCFi5hZizVJeDzxwb2vb+OxEZrW8DZ2dlyj6Wf0V//HTdunEGReNagXtoxC3sKTxoUiXBGErBwyY4DVSgsq2sph4WYMG14NwMjMkZ0ZDgiIlqTcGlpKY4dO9bGHsKXlJeXa27JISKMHj3auIA8aFCaNgHvLZQWsK+RBCxcsnRLkaY8dUhSwNw32RFEhKFDh2rq5HYk/6G//jtgwADExQXmbXT6yXH2HJEWsK+RBCza1dhkxfJt2mWhzx+bYlA0xhs+fLimLNeB/UewdD8D9l3Qucdq0Ngk85f7EknAol3r95WjrLqhpRwTYcbkQUkGRmQsScD+Sz8AK5ATcLfYMM0gSUujFQVFtW3sIbxNErBol37w1dkjkhEWGrxvHX0CzsnJQUNDg5Otha+orKzEvn37NHVjxowxKBrvGJSm7YbedajKoEiEI4Z8ihJRAhF9RkQ5RLSbiE41Ig7RvjpLE37ILtHUBePoZ1upqalITW19DSwWi90Hu/A9W7du1YxY79+/P+LjA2MBBmeGp+vmLz8oCdiXGNWMeRHAUmYeDGAUgN0GxSHasWpXKWotrSsfpcSFBcyqMV0h3dD+Z+PGjZpyIN7/q6dPwNsPSAL2JV5PwEQUD+B0AG8CADNbmLnc23EI1+hHP58zOgUmU+CvfNQeScD+Rz8COhhmMNMn4Lxj1WiSgVg+w4gWcCaAYgBvE9EWIppPRIG9mKyfqqhuwJqcMk3d+WOCu/u5mSRg/1JTU2O3IEGg3v9rKy4qFBkprbO3MYCqahmI5SuMSMAhAMYCeI2ZxwCoBvCofiMiuoOINhLRxuLiYv3DwgtW7jiBJmvrNbO+KZF29xYGq8GDB8Nkav3zOXjwICorKw2MSLRl165dsFpbW37p6elISgqOkfz6ZTQrq+ucbCm8zYgEfBjAYWb+VS1/BiUhazDzPGYez8zjU1KC955TI+nv/T13dAqIpPsZAKKiouzWXJVWsO/avn27pjxy5EiDIvG+EX213dDSAvYdXk/AzHwMwCEial6w8mwAu7wdh2hb2UkLNuaVa+rOGS1fhGxJN7T/2LZtm6YcVAlYdx246mStzF/uI4waBf1bAAuIaDuA0QCeMSgO4cQP2SWw6X1G/x7RyEgNrEXLu0oSsH+wWq12LeBguP7brF+PaESFm1vKDY1NOHz4sIERiWaGTObLzFsBBP4QRD+2Qtf9PH1kskGR+C5HCZiZpZveILdeexmKCgvs6qtr67Fl5/6Wstlsxn23XK75fzpQkA/A+fq6/sxkIgzrE4sNueUtdVu2bEGfPn2MC0oAMCgBC9/mqPt5+ihJwHoZGRmIjo5GdbWyykxlZSUOHTqE9PR0gyMLTkWFBQ4XqV/46zH89UTrlIyTByXipdt6a7YZem+Ox+Mz0tisOE0CXrduHWbNmmVcQAKATEUpHJDuZ9eYTCYMGzZMUyfd0L5n+wHt6PSRfQNz9aO2TByQqCmvX79eMypcGEMSsLAj3c+u03dDy9KEvmdbgS4BZ8Q62TJwDesTi5iI1uvA5eXlMn2qD5AELDSk+7ljRowYoSnrR9sKY5VXN+BAcettNyYChqcHXwvYbCaM75egqfv1118dbyy8RhKw0JDu544ZNWqUprxv3z6cPCkLn/uKHbru5wE9tSOCg8nEgQmasiRg40kCFhrS/dwxcXFxyMrKaikzs90tL8I4+u7nURnB1/ptpr8OvGXLFlgsFoOiEYAkYGHD0tAo3c+doL+ndOvWrYbEIezpV/8JxgFYzfokR6BHQnhL2WKxYP369QZGJCQBixalFSel+7kTJAH7psYmq936tyODuAVMRDhtsHb+6++++86gaAQgCVjYKC2v1pTPGtHNoEj8iz4BZ2dno6GhwZhgRIu9hdWwNLbeapMcG4aeieFt7BH4zhmt7dH68ccfUV9fb1A0QhKwAKB0R5VXaRPwGcMkAbuiZ8+eSE1tXabRYrEgJyewJ3bwB46u/wb7LGVjMuMRFto6/1JNTQ1+/vlnAyMKbpKABQBg06ZNmoW6U+LCMDBNlh50BRHZtYL1i78L79NPwBHMA7CamUyElCTt67Bs2TKDohGSgAUAYPXq1ZrylCFJQd9a6Ah9At64caMxgYgWdhNw9A2+CTgcSU7Uvg4///yz3DpnEEnAAsxs1w01dUhwLFbuLuPHa9cW2bx5s1wHNtCxsjoUVbTeYhMWYsKgXjEGRuQ7YqMjkJaW1lK2WCz46quvDIwoeEkCFti/fz8KCwtbymEhJkzon2BcQH4oMzMTycmtA1zq6upkXmgD6W8/Gto7BqEh8nEHKJdMZs6cqan78MMP0djYaFBEwUvekcKu9Tu+Xzwig3S2oM4iIpxyyimaOrnH0jgyAUfbrrjiCoSFta4Qdfz4caxYscLAiIKTJGBhd/136lDpfu4MScC+QwZgtS0hIcFuOcL33nsPzOxkD+EJsh5wkKusrLRbQGDKYEnAnaFPwNnZ2aipqUFUlExm4k219U3Yc0Q7qGiEDMBqkZeXj5nTxqK2zoLcvfsBNenm7tmFU0cPsBslbSs1LQNvLvjCW6EGPEnAQW7t2rWadUH7dY9Cz6QIAyPyX6mpqejbty8OHDgAAGhqasLmzZsxZcoUgyMLLrsOV2lmdEtPjkRiTJjzHYJNkwWL7usFAPj9e7VYuaOk5aGUsCp8dvdAhIc6vgQ186UCb0QYNKQLOshJ97N76VvBa9asMSiS4CW3H7nuN+dlwGxqvd3waFk9Fqw6YmBEwUUScBBramrCL7/8oqmbOkRmv+qKyZMna8o//fSTpodBeJ5dApbrv071TY3CVaelaereXnkYx8rqDIoouEgCDmLbt29HZWXrh1VIiFmulXXRKaecgsjIyJZyUVGRTEvpRVYr292CJAOw2nbb9D6Ij2q9GllracLzC/MNjCh4SAIOYvru56T4GJhMMvtVV4SHh+PUU0/V1P3www8GRRN89hfVoKq29X7W2MgQZMqKXm2KiwrFb87P0NT9tKsEP2afcLyDcBtJwEFMf/9vUrzM/ewOZ555pqb8448/GhJHMNq6X3f7Ud84+VLpgktO6WG3VvI/Fuahpr7JoIiCgyTgIHXkyBHk57d2M5lMJiTESQJ2hylTpsBsbh1Fun///paR0cKz9Nd/R2dK97MrTCbCHy/vrxmQVVRhwevL5H3rSZKAg5S+9Tt69GiEhsjsV+4QFxeHcePGaepk4XPvsGsBy/Vfl/XvGY3rTu+lqfv45yPIOSwLNXiKJOAgZbf4wtSpBkUSmM4++2xN+auvvpLR0B5Wb2lAoc3o3VAzYVgfGVTYEbfPSEdaYus8AFYGnvl8H6xWmSHLEwxLwERkJqItRPSNUTEEq5qaGrvl8iQBu9c555yjmWv32LFjWLdunYERBb7Kk7Wa8pDesQgLlTZGR0SEmfHoZf00dbsOn8SK7TIgyxOMfHfeD2C3gecPWuvXr9cslde7d2/07dvXwIgCT2xsLM455xxN3ZdffmlQNMFBn4Dl+m/nTB6chLNHJGvqXl92AE1N0gp2N0MSMBH1BnAhgPlGnD/Y6W8/mjJlCohkpKi7XXbZZZryTz/9hBMnpCXhKRX6BCzXfzvtN+dnwHbw+METtfh203HjAgpQRrWA/w/A7wE4vShGRHcQ0UYi2lhcXOy1wAKd1WqV679eMmLECGRlZbWUrVYrPvzwQwMjClw1NTWorq3X1MkMWJ2XnhKJWRN6aOrmLT8o14LdzOsJmIguAlDEzJva2o6Z5zHzeGYen5KS4qXoAt+ePXtQUtI6+XpUVBTGjBljYESBi4jsWsEff/wxioqKDIoocO3YsaNlVR8AyEyNQkJ0qIER+b/bpvdBqLm1GXysvB4l5VVt7CE6yogW8GkAZhFRAYCPAZxFRB8YEEdQ0nc/T5o0STNYSLjXJZdcguTk1utpFosF8+fLlRd327p1q6Ys13+7rkdihF0r+MjxMoOiCUxeX46QmR8D8BgAENGZAB5h5uu8HUew0nc/y1J5HdO8lmpHnCguR+6BY7A0NCIsNAT/eG43Vny9ANGR4S4fQ9ZhbZs+Acv9v+5x9dQ0fL7uaEu5qroWO3bswIgRIwyMKnDIesBB5MSJE9i1a5emThJwB9mspeqqxqaeuOL5GizdUoT+3ZV5iVPMZXjv7tFO113Vk3VYnWtsbFS6oG3IACz3yEiNwuRBiVi7p7Xl+9FHH0kCdhNDb5Jj5h+Z+SIjYwgm+u7noUOHIilJ1v/1tBCzCfdekKmpyzteg39+JSvOuMPevXtRV9c6AUe32DD06hbRxh6iI66eqv3CuWLFCpSWlhoUTWCRu9SDiH5RgNNPP92YQILQWSO6ISZc29r98tdjeGPZATDLyNKusO9+jpXb6txo0sAE9E1pXWLTarVi6dKlBkYUOCQBB4mamhqsX79eUzdt2jSDogk+RIRuMSFIT47U1P93xUG8+M1+ub2jC+wGYGXEGxNIgCIiXKwbjPX111/LF0c3kAQcJNauXWs3+5XtParC80wmwnPXD0aUriX8waojuGfeDpyotBgUmf+yWq1206rKCGj3u2BcqmZijtzcXOzZs8e4gAKEJOAgoV8Uftq0adJNZ4CBaTF49fbhiInQJuGNeRW4+oXNWLdHbvPoiNzcXFRWtq6AFBNhxuBeMQZGFJiS48Jw6qBETd2iRYsMiiZwSAIOAg0NDXa3H+kXjRfeM6JvHF6/cySSYrQTRZRVN+C387PxnyUFMu+uizZs2KApj8tKgMkkXyw9Yeb47pry0qVL0djYaFA0gUEScBDYsGEDqqurW8pJSUlyG4HBBveOwYcPjsWE/gmaegbw1spDuOP17TheXu9wX9FKn4AnDJDrv55y+tBuCLFZM7yiosLu9RcdIwk4CCxbtkxTPuOMM2AyyX+90ZLjwvDq7cNx5znp0DfathVU4voXt2BvoSyG7kxjYyM2b96sqdN/oRHuExZqQrcEbff+ihUrDIomMMincICrr6+3u/6rXyZPGMdkItw+oy9eu3MEkmO1U4KWnmzAna/vwI4DlU72Dm67d+9GTU1NSzk0NARZ6kQnwjOSE2M15R9++EG6obtAEnCAW7NmjeZDqlu3bhg3bpyBEQlHxvVLwEcPjcFk3UCXqtpG/Oa/2ThZU+dkz+Cl7/5MiI2SgYUelhAbjbi41lHmlZWVdqPQheskAQc4fffzjBkzpPvZRyXGhOH/bhmGa3QzD9XUN2Fn7mEcPy7rsdpat26dphwfK61fTzOZyG4Ap3RDd558Egewmpoau+knzz33XIOiEa4wmQgPzszE7dPTNfUWSyPuu+8+1NbWOtkzuFRVVdlNwJEYF21MMEFm+vTpmrJ0Q3eeJOAAtmzZMlgsrZM7pKWlYfjw4QZGJFxBRLjjnHTMOS1NU5+Xl4dnn31WZiCC0vq1Wq0t5czMTESEy/q/3jBhwgTExrZeC66oqMCmTW0u7y6ckAQcwL788ktN+fzzz5drZH6CiPDQrCycNaKbpn7x4sVYuHChMUH5kDVr1mjKp512mkGRBJ/Q0FDphnYTScABas+ePZqlB4kIF198sYERiY4ymQh/uWqQ3cje559/Hvn5wbuSktVqxdq1azV1sqymdznqhm5qajIoGv8lCThA6Vu/kyZNQlpampOtha+KDDfj79cPgdnc+qdqsVgwd+5czdzewSQnJ0ezHF5UVBRGjx5tXEBB6JRTTtF0Q5eXl0s3dCdIAg5ANTU1WLJkiabu0ksvNSga0VWZ3aPQL107DWBOTg7mz59vUETG0k+rOmnSJISEhBgUTXAKDQ3FGWecoamTbuiOkwQcgBYuXGg39aSs/evfUpPi7Lr93n77bezYscOgiIzBzHYf9NL9bAz9+3HlypXSDd1BkoADTGNjIxYsWKCpu/TSS6WF4OeICI899hiSk5Nb6qxWK+bOnRtUtybl5uZqrn+bzWb5cmmQiRMnIiamdWrK8vJybNmyxcCI/I8k4ACzbNkyzYQNYWFhuOqqqwyMSLhLfHw85s6dq6k7dOgQXnzxRYMi8r7vvvtOU544cSISEhKMCSbIOeqG/v777w2Kxj9JAg4gVqsV7777rqZu1qxZSEpKMigi4W6TJ0/G7NmzNXWfffaZ3ajgQMTMdjO7ycQyxjr77LM15e+//15zf7ZomyTgAPLdd99puudMJhOuv/56AyMSnnD//fcjPV07U9ZTTz2FiooKgyLyjuzsbBQWFraUw8LCZF1rg02aNAlRUa23yZWWltrNUCackwQcICwWC1599VVN3fTp09GrVy8newh/FRkZib/85S+aOb1PnDiB5557LqBnyfr222815SlTpiA6WqafNFJYWJjdNXjphnadjMzxQbdeexmKCgs6tM/hY6XYf7gIIWHhyMjsh5CQENx9992eCVAYbsSIEbj55pvx5ptvttQtX74cZ5xxBs477zwDI/OMyspKfPPNN5q6QHye/mj69OlYunRpS/n777/Hww8/LIu+uEASsA8qKizAovtcb7mWVllw+fMH0b97GHKP1wMAZs+ejT59+ngqROEDbr/9dqxZswY5OTktdX//+98xduxYpKamGhiZ+y1cuBB1da1LMqakpGDq1KkGRiSanXrqqYiKimpZ9vTEiRPYsmWLLHvqAvmKEgD+9XU+qmpbVyOJjo7GbbfdZmBEwhtCQkLw1FNPISwsrKWuqqoKTzzxREANhGlsbMTHH3+sqbvyyisRGiqLL/iC8PBwu2vxti1i4ZzXEzAR9SGiH4hoFxHtJKL7vR1DIFmbU4plW4s1dbfddpvcmhEksrKycO+992rqNmzYgHnz5hkUkfutWLECRUVFLeXw8HBcdtllBkYk9PSj0b///nvNSmzCMSNawI0AHmbmoQAmAfgNEQ01IA6/V1XbiGc+z9XURUdF4JprrjEoImGEOXPmYMKECZq6+fPn203Z6I9qa2vx8ssva+ouvPBCxMfHGxSRcGTixIma/5PKykqsW7fOwIj8g9cTMDMfZebN6u9VAHYDkKG6HcTMeObzfThWXt9SZyJgQN/uMJvNBkYmvM1kMuHpp5+2u9/7T3/6k9+vmjR//nzNxDJmsxnXXXedgREJR0JCQjBjxgxNnXRDt8/Qa8BElAFgDIBfjYzDHy3acBzLt53Q1F0ztRdioyMNikgYKTk5Gc8995xm5OnJkyfxwAMPaFYO8id79+7FBx98oKm75ppr7O6BFr5BPyr9xx9/RGVlpUHR+AfDEjARxQD4HMADzGz3v0REdxDRRiLaWFxcbH+AIFZQVIN/LMzT1A1Mi8Y952UYE5DwCWPHjsUDDzygqSssLMRDDz3UMkLVXxw6dAi//e1vNZP7p6am4vbbbzcwKtGWkSNHapY8tVgs0gpuhyEJmIhCoSTfBcz8haNtmHkeM49n5vEpKSneDdCHWRqs+OOCHNQ1tI5yjQg14dlrByMsVAa1B7urr77abunJ7OxsPPLII34zKGb9+vW4++67UVJSoql/6KGHNLMuCd9iMpkwa9YsTd3ChQsDenKYrvL6fcBERADeBLCbmV/w9vn93StLCrC3sFpT97tL+qFvqvLBlJeXj5nTxnbq2AcK8iGX4/0bEeEPf/gDjhw5gvXr1wMACvbnIXfPLixd9BmGZKW5PEFCaloG3lzg8Ptxu2wnk2lobELlyVqcrKmDpaERjY1NICKYTASTyQST+rvVyqg4WYO6BkZGZj/N8a666iq7eYeF97X3+VJvaUDu3nxATbq5e3Zh2sShiI2O7NL7KVAZMRHHaQCuB7CDiLaqdX9k5sUGxOJX1uaU4sPVRzR1M0YlY9YEm8XamywdmsTD1tB7c9rfSPi8kJAQPP/887jrrruwe/duNFrq0b97GAALsqIr8M+bhiA8tP2BejNfKuh0DMcO78cDZ4bjy1+PYcOhcjCAMABhJvUXPbVDJzIGyD2ubalfeOGFePjhh6F8dxeGcuHz5cG3qrF6d+u4gxkDGI/P7tWl91OgMmIU9M/MTMw8kplHqz+SfNtRUmXBEx/v1dT1TAzHHy8fIB9Mwk50dDRefvllZGZmaup/2VuGe/+bjcqaBo+c12q1YvHixdi0Mx+PLcjB+lwl+XbW7NmzMXfuXJnW0I9cMrGHprx4UxHKqz3zfvN38q72A1YrY+5He1Bm8yY2EfDXqwcjNlJmExWOJSQk4LXXXkNUZLimfsv+Stz8yjYcKal16/mys7Nxww03YO7cuair79oH7qhRo/Duu+/i0Ucfldvq/MyUwUlIjW/t5qhvtOLTNYVt7BG85NPbDyxYdQS/7ivX1N0xoy9GZcYZE5DwG8nJyRgxsA8SUYLcY61jBw4U1+L6F7fi6asH4bQhXVsvuqKiAq+++iq+/PJLpwNu+nWPwrh+8chIjUJ8VAisDFgarahvUH4sjVY0WRkZqVF4fnm1ZpEJ4V/MZsI1U3vh/77Z31L3yZpChCf0NjAq3yQJ2MftOlSFV5cUaOrGZsXjlrNloQXhmrDQEMy7bQQeeXc3Nue3rhlcWduI+9/aiWtP74W7z+2LiLCOtTStVisWLVqEl156yeFaxCEmwqwJ3XHlaWno39P1ZQNfXuUfo7WFc5dO7IH5Kw7iZJ1yG1lFTSO4KbDXq+4M6YL2YTX1TXh8QQ4ara2tirjIEDx99SCYTHLdV7guLioUr9w2HOeOtr+lb8GqI7jm31uweleJy7eMZGdn47bbbsPTTz/tMPkmJ8Xh89+Pwx9nD+hQ8hWBIToiBLNP7ampO3SsVLOilZAE7NP+8WUuDpVo37B/umIAuieEO9lDCOfCQk346zWD8MBFmdB/fzt4ohYPvr0Lt766HUs2F6HO0mS3f11dHVauXIl77rkHN910E7Zv3263TXp6Ol599VUMyUpDr24yK1swmzOlF0LNrW80i6UB77//voER+R7pgvZRSzcX4ZtNRZq6yyb2wFkjkg2KSBjJXfd3ExGuO6M3hvaOxROf7MHRsnrNttsPVGL7gUqEmgm7D1ejf3p3gAj19Q2orq13usyhyWRCn57dUF/SiL8+erfcUy6QHBeGOVPS8P5PrbdOvvvuu7jkkksgkyspJAH7oJo6C5754oCmLqt7FB6alWVQRMJwbr6/e2y/eHzy8Di8+M1+fLHuqN2tQg1NjPqGRiSEqQk6FECM44+LM4d1w8OzstAzKaLNc4rgc8vZ6Vi0sfU2pLq6Orz44ov461//anBkvkG6oH1MbW0tducdQU19axdgWIgJf7tmUIcHyQjRlqhwMx67vD8+fHAMpgzu+EjocVnx+M8dw/HPm4Zqkq8QzWIjQ3DnOdrFM5YuXYrvvvvOoIh8i7SAfQgz49lnn0VNbT0Q13of3UMzMzEgLcbAyEQgG5AWg/+7dRjyj1Xjm01F+G5rsWaZS1spcWGYPjIZ541JxbD0WC9HKvzRZRN74ot1xzQznD377LMYNWoUunfv3saegU8SsA9ZuHAhFi/WTgp23pgUXK4bTSiEJ2T1iMZ9F2bivgszUVJlwbiHf8Rz1w1Gk5URHxWKrO5RSIkPk5nXRIeYzYSn5gzE0u3rW+qqqqrw8MMPY968eUG9wIZ0QfuIPXv24Pnnn9fUZaZGyVSTwhDdYsMQFWbG9FEpOHdMKiYNSkRqQri8F0WnDEiLQWYv7cCrnJwcPPLII2hoCN5pKiUB+4Di4mI89NBDmuXiIsPM+McNQxAVLtd9hRD+Ly01EVOmTNHUrV+/3i/Xq3YXScAGq6mpwf3334/jx49r6h+/vD8yuwdv14wQIrAQEZ555hkMHTpUU//LL7/g9ttvR1FRkZM9A5dcAzZQbW0tHnroIezdq13lKC01EeeNTTUoKiGM1dl7nuXeY98XFRWFF198EbfeeisOHjzYUr9nzx7MmTMHTz75JE4//XQDI/QuScAGqa2txQMPPIBNmzZp6k8//XTs3fyDQVEJ4QM6ec+z3HvsHxITEzF//nzcf//92L17d0t9ZWUlHnroIcyaNQsPPvggYmMDf5S9dEEb4MSJE7jzzjvtku/QoUPxt7/9TQa6CCECWlJSEt544w1MnTrV7rGvv/4aV155JVavXm1AZN4lCdjLdu3ahRtvvBG7du3S1A8aNAivvPIKIiNl/lwhROCLiorCv/71L9x33312az4XFxfjwQcfxNy5cx0u9hEoJAF7SVNTE95++23cfPPNdgOuBg0ahNdeew1xcbK+rxAieJhMJtxwww146623kJmZaff44sWLccUVV2DlypUGROd5koC9IDs7GzfccANeffVVNDVpV5mZPHky5s2bJ8lXCBG0hg0bhgULFuCWW26ByaRNS6Wlpfj973+PRx99FKWlpQZF6BmSgD2osLAQf/7zn3HTTTdhz549do9fffXV+Pe//43oaFkvVQgR3MLCwnDPPffg3XffxYABA+weX7FiBa644gosW7bM5XWrfZ0kYA84duwYnn32WVx22WVYsmSJ3eMJCQl44YUX8PDDD9td+xBCiGA2ZMgQvPfee7jjjjvsPh8rKirw+OOP45FHHsGJEycMitB95DYkNzp69CjefvttfP3118jdtweNFvsJ7VOS4tAtwoLnn3gAzzs4BiD3MwohAk9n7u+OQh227S2AVdfgzd2zC+++NQ9ZvVOR2i3O6Z0jqWkZeHPBF50N2eMkAbtBYWEh3nrrLSxatKjlGm+jpR79u7euaNS/RzQempWJUwYktns8uZ9RCBFwOnl/95Df7McfLh+EecsPwtJo1TxmrS1BVrQVj88egO4J4Xb7znypoLPReoUk4C7IycnB+++/j+XLl8NqtTrcJi0xArfPSMeF41JhMsn9vUII0RFEhJvO6oMzhnXDU5/uxY6DVZrH1+4pwxX/3IRbz+6Dq6f0Qlio/1xZlQTcQU1NTVi7di0+/PBDbNiwwel2EeGhmHvFAFwwLhUhZv95QwghhC/K7B6FN38zCh//XIhXlxSg3qY1XFPfhJcXF+CLdcdw+4x0nD8mFWaz7zd4JAG7aO/evfjmm2+wdOnSNofC9+7dG7fccgve+NcTmHVKDy9GKIQQgc1kIlxzei9MHZqEp/+3D5vztZN0HCmtw5Of7MW87w5i9qk9YWlocnIk32BIAiai8wC8CMAMYD4zP2dEHM4wM4qKirBz505s2rQJv/zyi2bicEcyMjJw00034fzzz4fZbMZ///2kd4IVQogg0yc5Eq/fOQJf/noMry4pQGVto+bxwrI6vLR4P3KLGnDPPfdgypQpGDt2LLKyshAWFubkqN7n9QRMRGYArwKYAeAwgA1E9DUz72p7z645ceIECgsLUVdXh/r6+paf2tpalJaWoqysDCUlJSgqKsKBAwdQVVXV/kEBjB07Ftdffz1OO+00uxvIhRBCeIbJRLj81J6YPjIZry87gC9+PYYm/XBpZqxfvx7r169X9zGhR48e6Nu3L/r06YOEhATExMQgNjYW0dHRCA8PR2hoKMLCwpCRkYGEhASPPgcjWsCnAMhl5nwAIKKPAVwMwKMJ+Ntvv8XLL7/slmNFRUVhxowZuOyyyzBs2DC3HFMIIUTHxUeH4g+X9cf1Z/bGW98fwuLNRXajpZtZrVYUFhaisLAQv/zyS5vH/dvf/oZzzz3XEyG3MCIB9wJwyKZ8GMBET580IiKiS/ubzWZMnDgRF110Ec444wyEh9sPeRdCCGGMtKQI/OmKAbj3ggx8tf4YlmwuRu5xS6ePFxoa6sboHCNvT+lFRLMBnMfMt6nl6wFMZOZ7ddvdAeAOtTgIgP1cjt6RDMD/p1zxH/J6e5e83t4lr7d3+cLr3ZeZUxw9YEQL+AiAPjbl3mqdBjPPAzDPW0E5Q0QbmXm80XEEC3m9vUteb++S19u7fP31NmLU0AYAA4gok4jCAMwB8LUBcQghhBCG8XoLmJkbieheAMug3Ib0FjPv9HYcQgghhJEMuQ+YmRcDWGzEuTvB8G7wICOvt3fJ6+1d8np7l0+/3l4fhCWEEEIIWQ9YCCGEMETQJmAiOo+I9hBRLhE96uDxcCL6RH38VyLKUOsziKiWiLaqP697PXg/5MLrfToRbSaiRvVWNdvHbiSiferPjd6L2n918fVusnl/ywBJF7jwej9ERLuIaDsRfU9EfW0ek/d3B3Xx9fad9zczB90PlMFfeQCyAIQB2AZgqG6bewC8rv4+B8An6u8ZALKNfg7+9OPi650BYCSA9wDMtqlPApCv/puo/p5o9HPy5Z+uvN7qYyeNfg7+9OPi6z0NQJT6+902nyfy/vbi662Wfeb9Hawt4JbpMJnZAqB5OkxbFwN4V/39MwBnE5Hvr2/lm9p9vZm5gJm3A9DPIXcugOXMXMrMZQCWAzjPG0H7sa683qLjXHm9f2DmGrW4Dsr8B4C8vzujK6+3TwnWBOxoOsxezrZh5kYAFQC6qY9lEtEWIvqJiKZ6OtgA4Mrr7Yl9g1VXX7MIItpIROuI6BK3RhaYOvp63wpgSSf3FV17vQEfen/LesAddxRAOjOXENE4AAuJaBgzVxodmBBu0peZjxBRFoCVRLSDmfOMDioQENF1AMYDOMPoWIKBk9fbZ97fwdoCdmU6zJZtiCgEQDyAEmauZ+YSAGDmTVCuRQz0eMT+zaXpRz2wb7Dq0mvGzEfUf/MB/AhgjDuDC0Auvd5ENB3A4wBmMXN9R/YVGl15vX3q/R2sCdiV6TC/BtA8InE2gJXMzESUoq5pDPUb1AAoAyeEc12ZfnQZgHOIKJGIEgGco9YJ5zr9equvc7j6ezKA0+DhpUIDQLuvNxGNAfAGlGRQZPOQvL87rtOvt8+9v40eBWbUD4ALAOyF0oJ9XK17Csp/GABEAPgfgFwA6wFkqfWXA9gJYCuAzQBmGv1c/OHHhdd7ApRrOdUASgDstNn3FvX/IRfAzUY/F3/46ezrDWAygB1QRpbuAHCr0c/FH35ceL1XADiufm5sBfC1zb7y/vbS6+1r72+ZCUsIIYQwQLB2QQshhBCGkgQshBBCGEASsBBCCGEAScBCCCGEASQBCyGEEAaQBCyEB9isuJJNRP8joigPnWc8Eb2k/n4mEU3uxDEeIKIb1N8Hq3FvIaJ+XYxtNBFdYFOe5WjlGhePlUJES7sSjxC+RhKwEJ5Ry8yjmXk4AAuAu1zZSZ11zWXMvJGZ71OLZ0K5z9Fl6vluAfChWnUJgM+YeQzbTM9Hio5+XoyGcr9mc6xfM/NzHTxG877FAI4S0Wmd2V8IXyQJWAjPWw2gPxElEdFCdY3SdUQ0EgCI6Ekiep+I1gB4n5Q1p1farGWarm53hdqi3kZEq9S6M4noG1LWq74LwINqC3YqEe0nolB1uzjbso2zAGxm5ka1tfoAgLuJ6Ac1jj1E9B6AbAB9iOg1dSL7nUT0l+aDENEEIlqrxraeiOKhTIxwlRrPVUR0ExG9om7v7Dm+Q0QvqcfKJ+1axQsBXOvG/xchDCUJWAgPUluY50OZdecvALYw80gAf4SyFm+zoQCmM/PVAF4G8K663QIAL6nbzAVwLjOPAjDL9jzMXADgdQD/Vlveq6HMc3uhuskcAF8wc4MuxNMAbFKPsdjmGNPUxwcA+A8zD2PmA1BmHRoPZS3hM4hopDod4CcA7ldjmw5lhq25UNZhHc3Mn+jO6+w5AkBPAFMAXATAtsW8EYCsPiYChiRgITwjkoi2QkkaBwG8CSWpvA8AzLwSQDciilO3/5qZa9XfT0Vrl/D76n4AsAbAO0R0O5RFydszH8DN6u83A3jbwTY9ARS3cYwDzLzOpnwlEW0GsAXAMChfHAYBOMrMG9TnVsnKEp5tcfYcAWAhM1uZeReA7jb1RQDS2jmuEH5DliMUwjNqmXm0bQURtbV9dXsHZOa7iGgilFbtJlKWw2xr+zVqV++ZAMzMnO0oTijznrcbFxFlAngEwARmLiOid9rZt7PqbX63fdEioMQrRECQFrAQ3rMa6jVMNSmeYMfrSK+F0mUMdfvV6j79mPlXZp4LpdXaR7dfFYBYXd17UFqajlq/ALAbQH8X44+DkpAriKg7lK51ANgDoCcRTVDjjFW73h3F08zhc2zHQCjXooUICJKAhfCeJwGMI6LtUK5t3uhku98CuFnd7noA96v1zxPRDiLKhpLAtun2WwTg0uZBWGrdAgCJAD5ycq4lAE53JXhm3gal6zkHSlJfo9ZbAFwF4GUi2gZgOZTW6g8AhjYPwnLxObZlGoBvXYlVCH8gqyEJEcDUUcQXM/P1bWzzJYDfM/M+70XWcerI74uZuczoWIRwB0nAQgQoInoZSjfxBcy8t43tBgHozsyrvBZcBxFRCoDTmHmh0bEI4S6SgIUQQggDyDVgIYQQwgCSgIUQQggDSAIWQgghDCAJWAghhDCAJGAhhBDCAJKAhRBCCAP8P6t27jruu4taAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['Porosity'].values,alpha=0.7,color=\"darkorange\",edgecolor=\"black\",bins=25,range=[pormin,pormax],density=True)\n",
    "sns.kdeplot(x=df['Porosity'].values,color = 'black',alpha = 0.8,linewidth=4.0,bw_method=0.10)\n",
    "plt.title('Histogram and Kernel Density Estimated PDF'); plt.xlabel('Porosity (fraction)'); plt.ylabel(\"Density\")\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=1.0, wspace=0.1, hspace=0.2); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the impact of changing the kernel width on the KDE PDF model? \n",
    "\n",
    "* let's loop over a variety of kernel sizes and observe the resulting PDF with the data histogram.\n",
    "* note, kernel width is controlled by bandwidth, but the bandwidth parameter is poorly documented in Seaborn and seems to be related to original standard deviation. My hypothesis is the kernel standard deviation is the product of the bandwidth and the standard deviation of the feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Band Width = 0.01, bandwidth x standard deviation = 0.0004968730570602708\n",
      "Band Width = 0.05, bandwidth x standard deviation = 0.0024843652853013543\n",
      "Band Width = 0.1, bandwidth x standard deviation = 0.0049687305706027085\n",
      "Band Width = 0.3, bandwidth x standard deviation = 0.014906191711808122\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i, bw in enumerate([0.01,0.05,0.1,0.3]):\n",
    "    plt.subplot(2,2,i+1)\n",
    "    print(r'Band Width = ' + str(bw) + r', bandwidth x standard deviation = ' + str(bw*np.std(df['Porosity'])) )\n",
    "    plt.hist(df['Porosity'].values,alpha=0.7,color=\"darkorange\",edgecolor=\"black\",bins=25,range=[pormin,pormax],density=True)\n",
    "    sns.kdeplot(x=df['Porosity'].values,color = 'black',alpha = 0.8,linewidth=4.0,bw_method=bw)\n",
    "    plt.xlim([0.0,0.3])\n",
    "    plt.title('Histogram and Kernel Density Estimated PDF, BW = ' + str(bw)); plt.xlabel('Porosity (fraction)'); plt.ylabel(\"Density\")\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=2.1, wspace=0.1, hspace=0.2); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cumulative Distribution Functions\n",
    "\n",
    "This method in GeostatsPy makes a cumulative histogram. \n",
    "\n",
    "* you could increase or decrease the number of bins, $> n$ is data resolution "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "GSLIB.hist(df['Porosity'].values,pormin,pormax,log=False,cumul = True,bins=1000,weights = None,xlabel='Porosity (fraction)',title='Cumulative Histogram',fig_name='hist_Porosity_CDF')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting a CDF with the matplotlib Package\n",
    "\n",
    "Here's how we can make a CDF with matplotlib.  \n",
    "\n",
    "* the y axis is cumulative probability with a minimum of 0.0 and maximum of 1.0 as expected for a CDF.\n",
    "* note after the initial hist command we can add a variety of elements such as labels to our plot as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['Porosity'].values,density=True, cumulative=True, label='CDF',\n",
    "           histtype='stepfilled', alpha=0.8, bins = 100, color='darkorange', edgecolor = 'black', range=[0.0,0.25])\n",
    "plt.xlabel('Porosity (fraction)')\n",
    "plt.title('Porosity CDF')\n",
    "plt.ylabel('Cumulation Probability')\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=1.0, wspace=0.1, hspace=0.2)\n",
    "#plt.savefig('cdf_Porosity.tif',dpi=600,bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculating and Plotting a CDF by Hand\n",
    "\n",
    "Let's demonstrate the calculation and plotting of a non-parametric CDF by hand\n",
    "\n",
    "1. make a copy of the feature as a 1D array (ndarray from NumPy)\n",
    "2. sort the data in ascending order\n",
    "3. assign cumulative probabilities based on the tail assumptions\n",
    "4. plot cumuative probability vs. value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The ndarray has a shape of (261,).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "por = df['Porosity'].copy(deep = True).values # make a deepcopy of the feature from the DataFrame\n",
    "print('The ndarray has a shape of ' + str(por.shape) + '.')\n",
    "\n",
    "por = np.sort(por)                           # sort the data in ascending order\n",
    "n = por.shape[0]                             # get the number of data samples\n",
    "\n",
    "cprob = np.zeros(n)\n",
    "for i in range(0,n):\n",
    "    index = i + 1\n",
    "    cprob[i] = index / n                     # known upper tail\n",
    "    # cprob[i] = (index - 1)/n               # known lower tail\n",
    "    # cprob[i] = (index - 1)/(n - 1)         # known upper and lower tails\n",
    "    # cprob[i] = index/(n+1)                 # unknown tails  \n",
    "\n",
    "plt.subplot(111)\n",
    "plt.plot(por,cprob, alpha = 0.8, c = 'black',zorder=1) # plot piecewise linear interpolation\n",
    "plt.scatter(por,cprob,s = 20, alpha = 1.0, c = 'darkorange', edgecolor = 'black',zorder=2) # plot the CDF points\n",
    "plt.grid(); plt.xlim([0.05,0.25]); plt.ylim([0.0,1.0])\n",
    "plt.xlabel(\"Porosity (fraction)\"); plt.ylabel(\"Cumulative Probability\"); plt.title(\"Cumulative Distribution Function\")\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.5, top=1.1, wspace=0.1, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In conclusion, let's finish with the histograms of all of our features!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VnT/uqYXAVT2PV7ZtkiRJkqRZaqtRvVCSewD/CDxpoqcnaKsJ2kiyDFgGsOeeew4sPkmSJEnSaI3yCOlewP2A85NcCewOnJfkPjRHRPfomXZ3YPVEC6mqY6tqSVUtmT9//pBDliRJkiQNy8gK0qq6sKruXVWLqmoRTRF6QFX9AjgVOCzJ3ZPcD9gbOGdUsUmSJEmSRm+Yl335FHAmsE+SlUleMtm0VXUxcBJwCfBV4JVVdfuwYpMkSZIkdW9o55BW1eEbeX7RuMdvBd46rHgkSZpukmwDnAHcnSYnf7aq3pxkZ+BEYBFwJfCcqrq2qzglSRqWkY6yK0mS7uRm4HFV9RBgMXBwkkcARwHfqKq9gW+0jyVJmnUsSCVJ6kg11rUPt25vRXN97hPa9hOAZ40+OkmShs+CVJKkDiXZMskKYA1welWdDexaVVcDtH/v3WGIkiQNjQWpJEkdqqrbq2oxzSXPlibZr995kyxLsjzJ8rVr1w4tRkmShsWCVJKkaaCqrgO+CRwMXJNkN4D275pJ5vHa3JKkGc2CVJKkjiSZn2TH9v62wBOAH9Jcn/uIdrIjgC90EqAkSUM2tMu+SJKkjdoNOCHJljQ7iU+qqi8mORM4qb2G98+BQ7sMUpKkYbEglSSpI1V1AbD/BO2/Ah4/+ogkSRotu+xKkiRJkjphQSpJkiRJ6oQFqSRJkiSpExakkiRJkqROWJBKkiRJkjphQSpJkiRJ6oQFqSRJkiSpExakkiRJkqROWJBKkiRJkjphQSpJkiRJ6oQFqSRJkiSpExakkiRJkqROWJBKkiRJkjphQSpJkiRJ6oQFqSRJkiSpE0MrSJMcl2RNkot62v41yQ+TXJDk80l27Hnu9UkuT/KjJE8eVlySJEmSpOlhmEdIjwcOHtd2OrBfVT0Y+DHweoAk+wKHAQ9q5/nPJFsOMTZJkiRJUse2GtaCq+qMJIvGtZ3W8/As4M/a+4cAn66qm4ErklwOLAXOHFZ8kiRJM9FNN65jwfwdBrrM3Rcu5JwVlwx0mdKm8j0+twytIO3DXwAntvcX0hSoY1a2bZIkSepx2/r1rD5mu4Euc8GbVg10edLm8D0+t3QyqFGSfwRuAz4x1jTBZDXJvMuSLE+yfO3atcMKUZIkSZI0ZCMvSJMcATwdeF5VjRWdK4E9eibbHVg90fxVdWxVLamqJfPnzx9usJIkSZKkoRlpQZrkYOB1wDOr6rc9T50KHJbk7knuB+wNnDPK2CRJkiRJozW0c0iTfAo4CNglyUrgzTSj6t4dOD0JwFlV9YqqujjJScAlNF15X1lVtw8rNkmSJElS94Y5yu7hEzR/eAPTvxV467DikSRJkiRNL50MaiRJkiRJkgWpJEmSJKkTFqSSJEmSpE5YkEqS1JEkeyT53ySXJrk4yd+07UcnWZVkRXt7atexSpI0DEMb1EiSJG3UbcBrquq8JPOAc5Oc3j737qp6R4exSZI0dBakkiR1pKquBq5u79+Q5FJgYbdRSZI0OnbZlSRpGkiyCNgfOLttelWSC5Icl2Sn7iKTJGl4LEglSepYku2Bk4Ejq+p64P3AXsBimiOo75xkvmVJlidZvnbt2lGFK0nSwFiQSpLUoSRb0xSjn6iqzwFU1TVVdXtVrQc+CCydaN6qOraqllTVkvnz548uaEmSBsSCVJKkjiQJ8GHg0qp6V0/7bj2TPRu4aNSxSZI0Cg5qJElSdw4EXgBcmGRF2/YG4PAki4ECrgRe3kVwkiQNmwWpJEkdqarvAJngqS+POhZJkrpgl11JkiRJUicsSCVJkiRJnbAglSRJkiR1woJUkiRJktQJC1JJkiRJUicsSCVJkiRJnbAglSRJkiR1woJUkiRJktQJC1JJkiRJUicsSCVJkiRJndiq6wAkSZKmi6WL92XlqlUDXeaN69YB2w10mZI0WwytIE1yHPB0YE1V7de27QycCCwCrgSeU1XXts+9HngJcDvw6qr62rBikyRJmsjKVatYfcxgi8d5R14/0OVJ0mwyzC67xwMHj2s7CvhGVe0NfKN9TJJ9gcOAB7Xz/GeSLYcYmyRJkiSpY0MrSKvqDODX45oPAU5o758APKun/dNVdXNVXQFcDiwdVmySJEmSpO6NelCjXavqaoD2773b9oXAVT3TrWzbJEmSJEmzVF8FaZL9hhxHJmirSWJZlmR5kuVr164dcliSJPVnBLlSkqRZp98jpP+V5Jwkf5Vkx814vWuS7AbQ/l3Ttq8E9uiZbndg9UQLqKpjq2pJVS2ZP3/+ZoQiSdJADSpXSpI0Z/Q1ym5VPTLJ3sBfAMuTnAN8pKpOn+LrnQocAbyt/fuFnvZPJnkXsADYGzhnYwu79JKLWTB/hymGsGG7L1zIOSsuGegyJUmz3wBzpSRJc0bfl32pqsuSvBFYDvwbsH+SAG+oqs+Nnz7Jp4CDgF2SrATeTFOInpTkJcDPgUPbZV+c5CTgEuA24JVVdfvGYrr11ltYfcxO/a5CXxa8abDXHpMkzR1TzZWSJM11fRWkSR4MvBh4GnA68IyqOi/JAuBM4C5JtqoOn2Rxj5+osareCry1n3gkSZpuNiVXSpI01/V7hPR9wAdp9vDeNNZYVavbPcGSJM115kpJkqao34L0qcBNY91ok2wBbFNVv62qjw0tOkmSZg5zpSRJU9TvKLtfB7bteXyPtk2SJDXMlZIkTVG/Bek2VbVu7EF7/x7DCUmSpBnJXClJ0hT1W5DemOSAsQdJHgrctIHpJUmaa8yVkiRNUb/nkB4JfCbJ6vbxbsBzhxKRJEkz05GYKyVJmpK+CtKq+n6SBwL7AAF+WFW3DjUySZJmEHOlJElT1+8RUoCHAYvaefZPQlV9dChRSZI0M5krJUmagr4K0iQfA/YCVgC3t80FmGQlSWLTcmWSPdrn7wOsB46tqvcm2Rk4kaa4vRJ4TlVdO6zYJUnqSr9HSJcA+1ZVDTMYSZJmsE3JlbcBr6mq85LMA85NcjrwIuAbVfW2JEcBRwGvG3jEkiR1rN9Rdi+i2XsrSZImNuVcWVVXV9V57f0bgEuBhcAhwAntZCcAzxpcmJIkTR/9HiHdBbgkyTnAzWONVfXMoUQlSdLMs1m5MskiYH/gbGDXqrq6nf/qJPeeZJ5lwDKAPffcc7OClzQ3LF28LytXrRroMm9ctw7YbqDL1NzRb0F69DCDkCRpFjh6U2dMsj1wMnBkVV2fpK/5qupY4FiAJUuWeFqNpI1auWoVq48ZbPE478jrB7o8zS39XvblW0nuC+xdVV9Pcg9gy+GGJknSzLGpuTLJ1jTF6Ceq6nNt8zVJdmuPju4GrBle5JIkdaevc0iTvAz4LPCBtmkhcMqQYpIkacbZlFyZ5lDoh4FLq+pdPU+dChzR3j8C+MJAg5UkaZrod1CjVwIHAtcDVNVlwITns0iSNEdtSq48EHgB8LgkK9rbU4G3AU9MchnwxPaxJEmzTr/nkN5cVbeMndOSZCuaa6tJkqTGlHNlVX0HmOyE0ccPNjxJkqaffo+QfivJG4BtkzwR+Azw38MLS5KkGcdcKUnSFPVbkB4FrAUuBF4OfBl447CCkiRpBjJXSpI0Rf2Osrse+GB7kyRJ45grJUmaur4K0iRXMMF5MFV1/4FHJEnSDGSulCRp6vod1GhJz/1tgEOBnQcfjiRJM5a5UpKkKerrHNKq+lXPbVVVvQd43HBDkyRp5jBXSpI0df122T2g5+EWNHuB5w0lIkmSZiBzpSRJU9dvl9139ty/DbgSeM6mvmiSvwVeSnOuzYXAi4F7ACcCi8aWX1XXbuprSJI0YgPNlZIkzQX9jrL72EG9YJKFwKuBfavqpiQnAYcB+wLfqKq3JTmKZvj81w3qdSVJGqZB5kpJkuaKfrvs/t2Gnq+qd23C626b5FaaI6OrgdcDB7XPnwB8EwtSSdIMMYRcKUnSrDeVUXYfBpzaPn4GcAZw1VRfsKpWJXkH8HPgJuC0qjotya5VdXU7zdVJ7j3VZUuS1KGB5UpJkuaKfgvSXYADquoGgCRHA5+pqpdO9QWT7AQcAtwPuA74TJLnT2H+ZcAygC23yFRfXpKkYRlYrpQkaa7o67IvwJ7ALT2Pb6EZfGhTPAG4oqrWVtWtwOeAPwGuSbIbQPt3zUQzV9WxVbWkqpZsYUEqSZo+BpkrJUmaE/o9Qvox4Jwkn6cZGffZwEc38TV/DjwiyT1ouuw+HlgO3AgcAbyt/fuFTVy+JEldGGSulCRpTuh3lN23JvkK8Ki26cVV9YNNecGqOjvJZ4HzaIbF/wFwLLA9cFKSl9AUrYduyvIlSerCIHOlJElzRb9HSKEZDff6qvpIkvlJ7ldVV2zKi1bVm4E3j2u+meZoqSRJM9XAcqUkSXNBX+eQJnkzzSVYXt82bQ18fFhBSZI005grJUmaun4HNXo28Eya8zypqtXAvGEFJUnSDGSulCRpivotSG+pqqIZpIEk2w0vJEmSZiRzpSRJU9RvQXpSkg8AOyZ5GfB14IPDC0uSpBnHXClJ0hRtdFCjJAFOBB4IXA/sA/xTVZ0+5NgkSZoRzJWSJG2ajRakVVVJTqmqhwImVkmSxjFXSpK0afrtsntWkocNNRJJkmY2c6UkSVPU73VIHwu8IsmVNKMHhmaH8IOHFZgkSTOMuVKSpCnaYEGaZM+q+jnwlBHFI0nSjGKulCRp023sCOkpwAFV9bMkJ1fVn44gJkmSZpJTMFdKkrRJNnYOaXru33+YgUiSNEOZKyVJ2kQbK0hrkvuSJKmxybkyyXFJ1iS5qKft6CSrkqxob08dWKSSJE0zG+uy+5Ak19Ps/d22vQ93DNRwz6FGJ0nS9Lc5ufJ44H3AR8e1v7uq3jHwSCVJmmY2WJBW1ZajCkSSpJloc3JlVZ2RZNEAw5EkaUbp9zqkkiRpdF6V5IK2S+9OXQcjSdKw9HsdUkmSNBrvB46hOR/1GOCdwF9MNGGSZcAygD333HNU8U0rSxfvy8pVqwa2vBvXrQO2G9jyJE0PN924jgXzdxjoMndfuJBzVlwy0GXORRakkiRNI1V1zdj9JB8EvriBaY8FjgVYsmTJnBx8cOWqVaw+ZnAF5Lwjr9/4RJJmnNvWrx/odwXAgjcNbmfYXGaXXUmSppEku/U8fDZw0WTTSpI003mEVJKkjiT5FHAQsEuSlcCbgYOSLKbpsnsl8PKu4pMkadgsSCVJ6khVHT5B84dHHogkSR2xy64kSZIkqRMWpJIkSZKkTthlV5IkaY4b9CUxvByGpH51UpAm2RH4ELAfzaANfwH8CDgRWEQziMNzquraLuKTJEmaSwZ9SQwvhyGpX1112X0v8NWqeiDwEOBS4CjgG1W1N/CN9rEkSZIkaZYaeUGa5J7Ao2lHEayqW6rqOuAQ4IR2shOAZ406NkmSJEnS6HRxhPT+wFrgI0l+kORDSbYDdq2qqwHav/fuIDZJkiRJ0oh0UZBuBRwAvL+q9gduZArdc5MsS7I8yfL162tYMUqSJEmShqyLgnQlsLKqzm4ff5amQL0myW4A7d81E81cVcdW1ZKqWrLFFhlJwJIkSZKkwRt5QVpVvwCuSrJP2/R44BLgVOCItu0I4Aujjk2SJEmSNDpdXYf0r4FPJLkb8FPgxTTF8UlJXgL8HDi0o9gkSZIkSSPQSUFaVSuAJRM89fgRhyJJkiRJ6khX1yGVJEmSJM1xFqSSJEmSpE5YkEqSJEmSOmFBKkmSJEnqhAWpJEmSJKkTXV32RZIkSZJmrJtuXMeC+TsMbHm7L1zIOSsuGdjyZgoLUkmSJEmaotvWr2f1MdsNbHkL3rRqYMuaSeyyK0mSJEnqhAWpJEmSJKkTFqSSJEmSpE5YkEqSJEmSOmFBKkmSJEnqhAWpJEmSJKkTFqSSJEmSpE5YkEqSJEmSOmFBKklSR5Icl2RNkot62nZOcnqSy9q/O3UZoyRJw2RBKklSd44HDh7XdhTwjaraG/hG+1iSpFnJglSSpI5U1RnAr8c1HwKc0N4/AXjWKGOSJGmULEglSZpedq2qqwHav/fuOB5JkobGglSSpBkqybIky5MsX7t2bdfhSJI0ZRakkiRNL9ck2Q2g/btmsgmr6tiqWlJVS+bPnz+yACVJGhQLUkmSppdTgSPa+0cAX+gwFkmShsqCVJKkjiT5FHAmsE+SlUleArwNeGKSy4Anto8lSZqVturqhZNsCSwHVlXV05PsDJwILAKuBJ5TVdd2FZ8kScNWVYdP8tTjRxqIJEkd6fII6d8Al/Y89rprkiRJkjSHdFKQJtkdeBrwoZ5mr7smSZIkSXNIV1123wP8AzCvp+1O111L4nXXJEmSZqCbblzHgvk7DHSZuy9cyDkrLhnoMiV1b+QFaZKnA2uq6twkB23C/MuAZQBbbpHBBidJkqTNdtv69aw+ZruBLnPBm1YNdHmSpocujpAeCDwzyVOBbYB7Jvk47XXX2qOjk153raqOBY4FuNvWW9aogpYkSZIkDdbIzyGtqtdX1e5VtQg4DPifqno+XndNkiRJkuaU6XQdUq+7JkmSJElzSGfXIQWoqm8C32zv/wqvuyZJkiRJc8Z0OkIqSZIkSZpDLEglSZIkSZ2wIJUkSZIkdcKCVJIkSZLUCQtSSZIkSVInLEglSZIkSZ2wIJUkSZIkdcKCVJIkSZLUCQtSSZIkSVInLEglSZIkSZ2wIJUkSZIkdcKCVJIkSZLUCQtSSZIkSVInLEglSZIkSZ2wIJUkSZIkdcKCVJIkSZLUia26DkCSJM0NSxfvy8pVqwa6zBvXrQO2G+gyJUmjY0EqSZJGYuWqVaw+ZrDF47wjrx/o8iRJo2WXXUmSJElSJyxIJUmSJEmdsMuuJEnTUJIrgRuA24HbqmpJtxFJkjR4FqSSJE1fj62qX3YdhCRJw2KXXUmSJElSJyxIJUmango4Lcm5SZZ1HYwkScMw8i67SfYAPgrcB1gPHFtV702yM3AisAi4EnhOVV076vgkSZomDqyq1UnuDZye5IdVdUbvBG2hugxgzz337CJGaWRuunEdC+bvMNBl7r5wIeesuGRgyxvGtXZvveV3bH23bQa2PK/dq+mmi3NIbwNeU1XnJZkHnJvkdOBFwDeq6m1JjgKOAl7XQXySJHWuqla3f9ck+TywFDhj3DTHAscCLFmypEYepDRCt61fP/Dr2C5402CLx2Fda3ft2+810OVJ08nIu+xW1dVVdV57/wbgUmAhcAhwQjvZCcCzRh2bJEnTQZLt2p22JNkOeBJwUbdRSZI0eJ2OsptkEbA/cDawa1VdDU3R2nZRkiRpLtoV+HwSaHL1J6vqq92GJEnS4HVWkCbZHjgZOLKqrm+Tbj/z/f58mS236G8eSZJmkqr6KfCQruOQJGnYOhllN8nWNMXoJ6rqc23zNUl2a5/fDVgz0bxVdWxVLamqJVtYkEqSJEnSjDXygjTNodAPA5dW1bt6njoVOKK9fwTwhVHHJkmSJEkanS667B4IvAC4MMmKtu0NwNuAk5K8BPg5cGgHsUmSJEmSRmTkBWlVfQeYrK/t40cZiyRJkiSpO52cQypJkiRJkgWpJEmSJKkTFqSSJEmSpE5YkEqSJEmSOmFBKkmSJEnqRBeXfZEkSZI6d9ON61gwf4eBLe/GdeuA7Qa2PGkusCCVJEnSnHTb+vWsPmZwBeS8I68f2LKkucIuu5IkSZKkTliQSpIkSZI6YUEqSZIkSeqEBakkSZIkqRMWpJIkSZKkTjjKriRJuouli/dl5apVA12ml8SQpMkN+jJEALsvXMg5Ky4Z6DIHzYJUkiTdxcpVqwZ6OQzwkhiStCGDvgwRwII3DXbH4jDYZVeSJEmS1AkLUkmSJElSJyxIJUmSJEmdsCCVJEmSJHXCglSSJEmS1AkLUkmSJElSJ7zsiyRJs8Cll1w80OvXec1QSZr5hnFt00GzIJUkaRa49dZbWH3MTgNbntcMlaSZb9DXNs1fDj432GVXkiRJktSJaVeQJjk4yY+SXJ7kqK7jkSSpC+ZDSdJcMK0K0iRbAv8BPAXYFzg8yb7dRiVJ0miZDyVJc8W0KkiBpcDlVfXTqroF+DRwSMcxSZI0auZDSdKcMN0K0oXAVT2PV7ZtkiTNJeZDSdKckKrqOobfS3Io8OSqemn7+AXA0qr6655plgHL2of7AReNPNDZaRfgl10HMQu4HQfD7TgYbsfB2aeq5o3qxfrJh237bM2Js+29O5vWZzatC8yu9XFdpq/ZtD4Dz4fT7bIvK4E9eh7vDqzunaCqjgWOBUiyvKqWjC682cttORhux8FwOw6G23Fwkiwf8UtuNB/C7M2Js2ldYHatz2xaF5hd6+O6TF+zaX2GkQ+nW5fd7wN7J7lfkrsBhwGndhyTJEmjZj6UJM0J0+oIaVXdluRVwNeALYHjqurijsOSJGmkzIeSpLliWhWkAFX1ZeDLfU5+7DBjmWPcloPhdhwMt+NguB0HZ+Tbcor5EGbX/3s2rQvMrvWZTesCs2t9XJfpazatz8DXZVoNaiRJkiRJmjum2zmkkiRJkqQ5YkYUpEkOTvKjJJcnOWqC55Pk39rnL0hyQBdxTnd9bMfntdvvgiTfS/KQLuKcCTa2LXume1iS25P82Sjjmyn62Y5JDkqyIsnFSb416hhngj4+2zsk+e8k57fb8cVdxDndJTkuyZokE146ZZS5ZnPyXpIrk1zYfm6W97TvnOT0JJe1f3caVvyDWp8k+7TrMXa7PsmR7XNHJ1nV89xTp8m6PDDJmUluTvLafubt6n+zqeuSZI8k/5vk0vY75W96nuvk/7I569M+N60+N5vxv5l2n5k+12fS36Az8HMz4brM4M/Nhv43g/ncVNW0vtEM5vAT4P7A3YDzgX3HTfNU4CtAgEcAZ3cd93S79bkd/wTYqb3/FLfjpm/Lnun+h+YcsD/rOu7pduvzPbkjcAmwZ/v43l3HPd1ufW7HNwBvb+/PB34N3K3r2KfbDXg0cABw0STPjyTXbG7eA64Edplguf8POKq9f9TYe2IE23Ugebxdzi+A+7aPjwZeO+L3SD/rcm/gYcBbe+Pb0Lxd/G82c112Aw5o788DftyzLiP/v2zu+rTPTZvPzeauy7jldPqZmcL6TPgbdIZ+biZbl5n6uZm0PhjU52YmHCFdClxeVT+tqluATwOHjJvmEOCj1TgL2DHJbqMOdJrb6Hasqu9V1bXtw7Nornunu+rnPQnw18DJwJpRBjeD9LMd/xz4XFX9HKCq3JZ31c92LGBekgDb0xSkt402zOmvqs6g2TaTGVWuGVbeOwQ4ob1/AvCsAca8IYNan8cDP6mqnw0/5En1k0vXVNX3gVunMG8X/5tNXpequrqqzmvv3wBcCiwcQcwbsjn/mw2ZUf+bcabDZwY27zfoTPzcTLguM/hzsyn1wZT+NzOhIF0IXNXzeCV3/ef1M81cN9Vt9BKavdW6q41uyyQLgWcD/zXCuGaaft6TDwB2SvLNJOcmeeHIops5+tmO7wP+EFgNXAj8TVWtH014s8qocs3m5r0CTms/M8t6ptm1qq6G5ocRzRGWURhUHj8M+NS4tle13ciOG1F3vc15D2xo3i7+NwN5PydZBOwPnN3TPOr/C2z++kynz82gvmumw2cGNu836Ez/3Ez4e3oGf27Gr89APjczoSDNBG3jhwbuZ5q5ru9tlOSxNG+41w01opmrn235HuB1VXX78MOZsfrZjlsBDwWeBjwZeFOSBww7sBmmn+34ZGAFsABYDLwvyT2HG9asNKpcs7l578CqOoCma9Urkzx6kMFtgs3O40nuBjwT+EzP8+8H9qJ5T18NvHOzouzP5rwHpttvlc2OJ8n2ND2Bjqyq69vmLv4vsPnrM50+N4P430yXzwxs3m/QGfu5mez39Ez93EyyPgP53MyEgnQlsEfP491p9vJPdZq5rq9tlOTBwIeAQ6rqVyOKbabpZ1suAT6d5Ergz4D/TPKskUQ3c/T72f5qVd1YVb8EzgAcbOvO+tmOL6bp+lxVdTlwBfDAEcU3m4wq12xW3quqsb9rgM/TdMkCuGasG2z7d1Rd4AeRx58CnFdV14w1VNU1VXV7e7T/g9yxnsO0Oe+BDc3bxf9ms97PSbam+VH9iar63Fh7R/8X2Mz1mWafm0F810yXzwxs3m/QGfm5mez39Ez93Ey2PoP63MyEgvT7wN5J7tfu7TkMOHXcNKcCL0zjEcBvxg4T6/c2uh2T7Al8DnhBVf24gxhnio1uy6q6X1UtqqpFwGeBv6qqU0Ye6fTWz2f7C8CjkmyV5B7Aw2nOudAd+tmOP6c5l4gkuwL7AD8daZSzw6hyzSbnvSTbJZkHkGQ74EnART3zHNHeP4Lm8zUKg8jjhzOu6+G4c0yfzR3rOUz9rMumzNvF/2aT16U9H/3DwKVV9a5xz3Xxf4HNW5/p9rnZnPfZmOnymYHN+w064z43k63LTP3cbGB9Bve5qRGP5rQpN5rR935MMwrUP7ZtrwBe0d4P8B/t8xcCS7qOeTre+tiOHwKupenatwJY3nXM0/W2sW05btrjcZTdTd6OwN/TjLR7EU33ls7jnm63Pj7bC4DT2u/Hi4Dndx3zdLzR/Hi7mmaQkJU0XZM6yTWbmvdoRko8v71dPDZv+9y9gG8Al7V/d55G79FJty1wD+BXwA7jlvmxdtoLaH787DZN1uU+7fvneuC69v49J5u3y//Npq4L8Eiabn0XcMdvhqd2+X/ZzPWZdp+bzXyfTavPTJ/rM+lv0Bn4uZlwXWbw52ay9RnY5ybtTJIkSZIkjdRM6LIrSZIkSZqFLEglSZIkSZ2wIJUkSZIkdcKCVJIkSZLUCQtSSZIkSVInLEglSZI0EEluT7IiyUVJPtNeQ3oYr7Mkyb+19w9K8iebsIwjk7ywvf/ANu4fJNlrM2NbnOSpPY+fmeSoTVzW/CRf3Zx4pOnOglRq9STRsduiKc7/irHEtplx7Jbki+39eyX53yTrkrxvc5e9CbGs62Oahya5MMnlSf6tvfBzv8v/ZpIlU5h+UZINXiza5C1JnbqpqhZX1X7ALTTXM9yoJFtN5UWqanlVvbp9eBAwpYK0fb2/AD7ZNj0L+EJV7V9VP+mZLkmm+nt5Mc21HcdiPbWq3jbFZYzNuxa4OsmBmzK/NBNYkEp3GEuiY7crpzJzVf1XVX10AHH8HfDB9v7vgDcBrx3Acofl/cAyYO/2dnCXwZi8JWna+DbwB0l2TnJKkguSnJXkwQBJjk5ybJLTgI8muW+Sb7TTfSPJnu10h7ZHXM9PckbbdlCSL7Y7j18B/G27M/lRSa5IsnU73T2TXDn2uMfjgPOq6rb2aOaRwEvbncCLklya5D+B84A9krw/yfIkFyd5y9hCkjwsyffa2M5JsgPwz8Bz23iem+RFYzuVN7COx7c7db+X5KdJ/qwn1lOA5w3w/yJNKxak0iSSbN8mi/PaI4CH9Dz3wjaZnJ/kY23b0Ule297fK8lXk5yb5NtJHti23yWpTuBPga8CVNWNVfUdmsJ0Q7E+qE2EK9q49m7bT2ljuDjJsp7p1yV5axvHWUl2bdvvl+TMJN9Pckwf22g34J5VdWZVFfBRmr3M46fbsk22F7Xb8m97nj60jf3HSR7VTr+o3W7ntbe77Plul/mvbawXJHl5z9OnYPKWpM60RyCfAlwIvAX4QVU9GHgDTa4Y81DgkKr6c+B9wEfb6T4B/Fs7zT8BT66qhwDP7H2ddufxfwHvbncmfxv4JvC0dpLDgJOr6tZxIR4InNsu48s9y3hs+/w+bSz7V9XPgH+sqiXAg4HHJHlwkrsBJwJ/08b2BODGNt4T23hOHPe6k60jwG7AI4GnA71HVJcDj0KapSxIpTtsmzu6636epgh8dlUdADwWeGcaDwL+EXhcm4D+ZoJlHQv8dVU9lObo5n+27ZMmVWgKQuDaqrp5irG/AnhvVS0GlgAr2/a/aGNYArw6yb3a9u2As9o4zgBe1ra/F3h/VT0M+EUfr7uw57Vo7y+cYLrFwMKq2q+q/gj4SM9zW1XVUpq9029u29YAT2y3/XO5c8Ie8xLgN22sDwNe1m4/MHlLUle2TbKC5nv458CHaYqsjwFU1f8A92qPJAKcWlU3tff/mDu60H6snQ/gu8DxSV4GbNlHDB8CXtzefzF3zjljdgPWbmAZP6uqs3oePyfJecAPgAcB+9IUrVdX1ffbdbu+qm7bSGyTrSPAKVW1vqouAXbtaV8DLNjIcqUZa0r99aVZ7qa2oAOg7d7zL0keDaynKbR2penm89mq+iVAVf26dyFJtqc5l+UzueN0yru3f8eS6knA5yaIYWMJcjJnAv+YZHfgc1V1Wdv+6iTPbu/vQdOl9lc05/V8sW0/F3hie/9AmiO00CTKt2/kdSc6X7QmaPspcP8k/w58CTit57mx7XAusKi9vzXwviSLgduBB0ywzCcBD+7p1rQDzfpdgclbkrpyp1wKzXmYE0w3litu3MCyCqCqXpHk4TRHPVe0uWHymaq+2/a0eQywZVVNNPbATcA2G1jM7+Nqd3a+FnhYVV2b5Ph23jBxzpuK3vl7d0b3brNt2nilWckjpNLkngfMBx7aJtdr6C8BbQFcN+581D+EJqkCb6QpDlf0HLEcs7EECUCSZ/cczV1SVZ+kOeJ6E/C1JI9LchBN96E/bo+E/qBn2be2XWyhKfh6d05NJbmuBHbvebw7sLrtTjsW3z9X1bXAQ2i6Ub2SZu/1mLEE3BvH39Js74fQHN2920SbgeYo9Ng2vl9VjRW6Jm9Jmj7OoD2Nos1Nv6yq6yeY7ns0XWxpp/9OO89eVXV2Vf0T8EuaHNrrBmDeuLaPAp9i4qOjAJcCf9Bn/PekKVB/057i8pS2/YfAgiQPa+Oc13ZVniieMROu40Y8ANjggH7STGZBKk1uB2BNVd2a5LHAfdv2b9B03bkXQJKde2dqk+wVSQ5tn0+Sh7T3N5ZUf8wdRwknVVWf7ynElie5P/DTqvo34FSac1x2oOn++9v2HNZH9LHO3+XOifL3kvxwgjiuBm5I8oh2D/gLaUYpvL0nvn9KsguwRVWdTDNI0wEbiWMHmm5Q64EXMHEXra8Bf5k7Bq54QJLt2udM3pI0fRwNLElyAc25kUdMMt2rgRe3072AO06J+dd2/IGLaIrb88fN99/A2I7asdM1PgHsRFOUTuQrwKP7Cb6qzqfZqXsxcBxNrqSqbqE5reTfk5wPnE6zQ/R/gX3beJ7b5zpuyGNpehdJs5JddqXJfQL47yTLgRU0e0KpqouTvBX4VpLbaZLUi8bN+zzg/UneSNP99NM0CfRf2wGHQlPY3impVtWNSX6S5A+q6nKAJFfS7J29W5JnAU9qzy/p9Vzg+UlupTn3859p9ua+ok16PwLOYuP+Bvhkkr8BTh5rbAvKyS7n8pfA8cC2NAn+KxNMsxD4SO4YOv/1G4njP4GT26L+f5m4S9eHaIr389pieC13DKhk8pakDlTV9hO0/Ro4ZIL2o8c9vpLmtJjx0/2fCV7qm+2NqvoxzY7YXo+kOb3mukni/FmSXyXZu6ou642ljWO/cdO/aJLlfJ+Jd/g+bNzj43uWPdE6vmjc497t+Ewm2H7SbJE7eu1Jmg7acz4fWlVv7DqWMUmeDty/PQI77aUZwfiQtquwJGkOaccreArw1LZYnWy6fYBdq2qyUe87l2Q+cGBVndJ1LNKwWJBK01CSl1bVhzY+pcYzeUuSJM0cFqSSJEmSpE44qJEkSZIkqRMWpJIkSZKkTliQSpIkSZI6YUEqSZIkSeqEBakkSZIkqRMWpJIkSZKkTliQStNQkhcl+c4Gnv9KkiMmmjbJuiT3H0Wc/ZogxkryB13GJEnaNEmel+S0ruPYVEkWtXloq65jGTTzrWYiC1LNekmuTHJTW6hdk+QjSbbvOq7NUVVPqaoTJnlu+6r6KUCS45P83015jSSvT/LlcW2XTdJ22Ka8xiSv+80kv0tyQ5Lrk5yb5Kgkd5/CMkzAkma89vvw2ql8/w0hhrsUb1X1iap60iYsa5Nz0mxmvtVcZ0GqueIZVbU9cADwMOCNU5k5jbn2eTkDODDJlgBJ7gNsDRwwru0P2mkH6VVVNQ/YDXgNcBjw5SQZ8OtI0rSUZBHwKKCAZ3YbjYbMfKs5ba79wNYcV1WrgK8A+wEkeUSS7yW5Lsn5SQ4am7bdc/jWJN8Ffgvcv90T+FftXsobkhyTZK8kZ7Z7F09KcreeZTw9yYp2+d9L8uCe545K8pN2OZckefa4cJPk35P8JskPkzx+XGwvnWgdx/ZWJlkGPA/4h/bo8H8n+fskJ4+b/t+TvGeCRX2fJiEubh8/Gvhf4Efj2n5SVauT7JDkw0muTrIqyf8dS6SbqqpurKpv0vwY+2PgaW3MS9ttfl37eu8b2+5JxpL1+e16PzfJTkm+mGRte7Thi0l235zYJGnIXgicBRwPHNH7RJI9knyu/U77VZL3te1bJHljkp8lWZPko0l2aJ87KMnKccu5MskT2vtLkyxvc9k1Sd7VTjb2nXpd+536x7lrt9AHJTk9ya/bed+wsZXLHUdeX5zkqva7+RVJHpbkgvb7/X09078oyXc3kBcnzUFJtkzyjiS/TPJT2lzSM++Lk1za5uOfJnl5z3MHJVmZ5DXtNr06yYt7nt82yTvbbf6bJN9Jsm373KS/McYx32pOsyDVnJJkD+CpwA+SLAS+BPxfYGfgtcDJSeb3zPICYBkwD/hZ23Yw8FDgEcA/AMfSFH570BS6h7evdQBwHPBy4F7AB4BTc0dXmJ/Q7P3eAXgL8PEku/W89sOBnwK7AG8GPpdk537XtaqOBT4B/L+2G+8zgI8DByfZsY1xK+C5wMcmmP8W4GyaJEj799vAd8a1jSWkE4DbaPbg7g88CZiwaJ6qqvo5sJxmewHcDvwtzbb5Y+DxwF+1047F9pB2vU+k+a77CHBfYE/gJuD3P3QkaRp6Ic13+CeAJyfZFZriCvgiTU5aBCwEPt3O86L29ljg/sD29P9d917gvVV1T2Av4KS2few7dcf2O/XM3pmSzAO+DnwVWECTA77R/2rycGBvmlz0HuAfgScADwKek+Qx46adLC9uKAe9DHh6274E+LNxMaxpn78n8GLg3W0OH3Mfmly9EHgJ8B9JdmqfewfNb4I/ofkt8Q/A+j5/YwDmW8mCVHPFKUmuo/ly/xbwL8DzgS9X1Zeran1VnU7zJfzUnvmOr6qLq+q2qrq1bXt7VV1fVRcDFwGnVdVPq+o3NEdf92+nexnwgao6u6pub8/5vJmmkKWqPlNVq9vXPhG4DFja89prgPdU1a3t8z9i3F7dqaqqq2kS2qFt08HAL6vq3Elm+RZ3JMNH0STIb49r+1b7Q+kpwJHtXtY1wLtpuv4MymqapE5VnVtVZ7X/lytpiv3HTDZjVf2qqk6uqt9W1Q3AWzc0vSR1KckjaX7Qn9R+P/8E+PP26aU0hd/ft9+3v6uqsaOVzwPe1eakdcDrgcPS3+A9twJ/kGSXqlpXVWf1Ge7TgV9U1TvbWG6oqrP7nBfgmHa+04AbgU9V1Zq2R9O3uSOnwiR5sY8c9Jx2vquq6tfA/9cbQFV9qap+Uo1vAadxR0E2tm3+uX3dLwPrgH3SnMrzF8DfVNWqNtd/r6pupr/fGL3Mt5qzLEg1VzyrqnasqvtW1V9V1U00yf7QthvKdW3B+kia8yjGXDXBsq7puX/TBI/HBky6L/Caccvfg+aHBElemDu6815Hc3R1l55lraqq6nn8s7F5N9MJNImS9u9djo72OAN4ZLsneH5VXQZ8D/iTtm2/dpr70nQ3urpnfT4A3HsA8Y5ZCPwaIMkD2m5Av0hyPc0Ohl0mmzHJPZJ8oO1SdX0b846b28VJkobkCJqdnb9sH3+SO7rt7gH8rKpum2C+BdzRm4f2/lbArn285kuABwA/TPL9JE/vM9Y9aArmTdVvToXJ8+LGctAC7pzPe7cRSZ6S5Kw0XY6voykae3PKr8Zt79+2ce0CbMPE69/Pb4xe5lvNWRakmsuuAj7WFqpjt+2q6m0909RkM/e5/LeOW/49qupTSe4LfBB4FXCvqtqR5mhr7yACC5M7DSqwJ81ey6mYKP5TgAcn2Y9mz/YnNjD/mTTdlJYB3wWoquvbOJYBq6vqinZdbwZ26VnXe1bVg6YY74TartYPpdlbDPB+4IfA3m33sjdw52033muAfYCHt9OP7XF20AZJ00p7/uFzgMe0RcAvaLpMPiTJQ2i+b/ec5KjnapqCZcyeNF07r6E5+niPntfZEvh999GquqyqDqcpbN4OfDbJdmw8D15F08V3FCbLixvLQVfTFM698wHQnkZzMk3X213bfPxl+ssPvwR+x8Tr389vjF7mW81ZFqSayz4OPCPJk9MMeLBNmsELBnXy/QeBVyR5eBrbJXlae77NWJJfC82ACrQDLfW4N/DqJFsnORT4Q5okORXX0JxH9HtV9TvgszR73M+p5nyRCbVHkpcDf8cdyQmars9/R3s+S9sV+DTgnUnumWZgjb3GnfszZe2e1scAXwDO4Y71nwdcD6xL8kDgL8fNOn6959Hsab+uPd/ozZsTlyQN0bNoztvbl2ZAm8U03//fpjmv9ByaAuttbV7ZJsmB7byfAv42yf3SXN7sX4AT26N7Pwa2afPQ1jSjzf/+8h5Jnp9kflWtB65rm2+nyVPrGZdLenwRuE+SI5PcPcm8JA8fwHaYyIR5sY8cdFI73+7t0cajepZ5N5rtsBa4LclTaM7J3Kh2Wx0HvCvJgva3xB+3Re6UfmOYbzWXWZBqzqqqq4BDaPb2raXZ6/j3DOhzUVXLac4jfR9wLXA5zWATVNUlwDtp9oheA/wR7R7RHmfTDPTwS5pzMP6sqn41xTA+DOzbdus5paf9hPY1N9Rdd8y3aH4EfKen7dttW+/w8y+kSeyX0KzvZ5m8a9LGvC/JDTTb5j00e68PbpM/NIND/DlwA03hf+K4+Y8GTmjX+zntMral2ZZn0Qy+IUnT0RHAR6rq51X1i7EbTS55Hs2RpmfQDGjzc2AlzYBA0BRHH6P5br6C5ujdXwNUM87BXwEfAlbRHDHtHXX3YODiJOtoBjg6rD2387c0Oei77XfqI3qDbc8TfGIb0y9oxkN47AC3R68N5cUN5aAPAl8DzgfOAz43Lv5X0xSt19LkllOnENNrgQtpRsr9Nc3R5S028TeG+VZzUu7cFV/SXJBkT5ouOPdpuwRJkjRtJXkR8NKqemTXsUgaLI+QSnNMmlEB/w74tMWoJEmSutTPMOCSZol2gIpraEYYPLjjcCRJkjTH2WVXkiRJktQJu+xKkiRJkjphQSpJkiRJ6sSMPod0l112qUWLFnUdhiRpljv33HN/WVXzu45jQ8yJkqRhG0Y+nNEF6aJFi1i+fHnXYUiSZrkkP+s6ho0xJ0qShm0Y+dAuu5IkSZKkTliQSpIkSZI6YUEqSZIkSeqEBakkSZIkqRMWpJIkSZKkTliQSpIkSZI6YUEqSZIkSeqEBakkSZIkqRMWpJIkSZKkTliQSpIkSZI6MbSCNMlxSdYkuain7cQkK9rblUlWtO2LktzU89x/DSsuSZIkSdL0sNUQl3088D7go2MNVfXcsftJ3gn8pmf6n1TV4qm8wKWXXMyC+TtsXpTj7L5wIeesuGSgy5QkSRqUpYv3ZeWqVQNdpr9/JHVlaAVpVZ2RZNFEzyUJ8BzgcZvzGrfeegurj9lpcxZxFwveNNgveEmSpEFauWoVq4/ZbqDL9PePpK50dQ7po4Brquqynrb7JflBkm8leVRHcUmSJEmSRmSYXXY35HDgUz2Prwb2rKpfJXkocEqSB1XV9eNnTLIMWAaw5RYZSbCSJEmSpMEb+RHSJFsB/wc4caytqm6uql+1988FfgI8YKL5q+rYqlpSVUu2sCCVJEmSpBmriy67TwB+WFUrxxqSzE+yZXv//sDewE87iE2SJEmSNCLDvOzLp4AzgX2SrEzykvapw7hzd12ARwMXJDkf+Czwiqr69bBikyRJkiR1b5ij7B4+SfuLJmg7GTh5WLFIkiRJkqafrkbZlSRJkiTNcRakkiRJkqROWJBKktSRJNskOSfJ+UkuTvKWtv3oJKuSrGhvT+06VkmShqGr65BKkiS4GXhcVa1LsjXwnSRfaZ97d1W9o8PYJEkaOgtSSZI6UlUFrGsfbt3eqruIJEkaLbvsSpLUoSRbJlkBrAFOr6qz26deleSCJMcl2am7CCVJGh4LUkmSOlRVt1fVYmB3YGmS/YD3A3sBi4GrgXdONG+SZUmWJ1m+du3aEUUsSdLgWJBKkjQNVNV1wDeBg6vqmrZQXQ98EFg6yTzHVtWSqloyf/780QUrSdKAWJBKktSRJPOT7Nje3xZ4AvDDJLv1TPZs4KIOwpMkaegc1EiSpO7sBpyQZEuancQnVdUXk3wsyWKaAY6uBF7eXYiSJA2PBakkSR2pqguA/Sdof0EH4UiSNHJ22ZUkSZIkdcKCVJIkSZLUCQtSSZIkSVInLEglSZIkSZ2wIJUkSZIkdcKCVJIkSZLUCQtSSZIkSVInLEglSZIkSZ2wIJUkSZIkdcKCVJIkSZLUCQtSSZIkSVInLEglSZIkSZ2wIJUkSZIkdcKCVJIkSZLUCQtSSZIkSVInLEglSZIkSZ2wIJUkSZIkdWJoBWmS45KsSXJRT9vRSVYlWdHentrz3OuTXJ7kR0mePKy4JEmSJEnTwzCPkB4PHDxB+7uranF7+zJAkn2Bw4AHtfP8Z5IthxibJEmSJKljQytIq+oM4Nd9Tn4I8OmqurmqrgAuB5YOKzZJkiRJUve6OIf0VUkuaLv07tS2LQSu6plmZdsmSZIkSZqlRl2Qvh/YC1gMXA28s23PBNPWRAtIsizJ8iTL16+fcBJJkiRJ0gww0oK0qq6pqturaj3wQe7olrsS2KNn0t2B1ZMs49iqWlJVS7bYYqI6VpIkSZI0E2w1yhdLsltVXd0+fDYwNgLvqcAnk7wLWADsDZwzytgkSZKGYenifVm5atXAlnfjunXAdgNbniR1aWgFaZJPAQcBuyRZCbwZOCjJYpruuFcCLweoqouTnARcAtwGvLKqbh9WbJIkSaOyctUqVh8zuAJy3pHXD2xZktS1oRWkVXX4BM0f3sD0bwXeOqx4JEmSJEnTSxej7EqSJEmSZEEqSVJXkmyT5Jwk5ye5OMlb2vadk5ye5LL2704bW5YkSTORBakkSd25GXhcVT2E5pJoByd5BHAU8I2q2hv4RvtYkqRZx4JUkqSOVGNd+3Dr9lbAIcAJbfsJwLNGH50kScNnQSpJUoeSbJlkBbAGOL2qzgZ2HbtMWvv33h2GKEnS0FiQSpLUoaq6vaoWA7sDS5Ps1++8SZYlWZ5k+dq1a4cWoyRJw2JBKknSNFBV1wHfBA4GrkmyG0D7d80k8xxbVUuqasn8+fNHFaokSQNjQSpJUkeSzE+yY3t/W+AJwA+BU4Ej2smOAL7QSYCSJA3ZVl0HIEnSHLYbcEKSLWl2Ep9UVV9MciZwUpKXAD8HDu0ySEmShsWCVJKkjlTVBcD+E7T/Cnj86COSJGm07LIrSZIkSeqEBakkSZIkqRMWpJIkSZKkTliQSpIkSZI6YUEqSZIkSeqEBakkSZIkqRMWpJIkSZKkTliQSpIkSZI6YUEqSZIkSerEVl0HIEmSNF0sXbwvK1etGugyb1y3DthuoMuUpNnCglSSJKm1ctUqVh8z2OJx3pHXD3R5kjSb2GVXkiRJktQJC1JJkiRJUicsSCVJkiRJnbAglSRJkiR1woJUkiRJktQJR9mVJEmS5ohhXNpo94ULOWfFJQNdpuYOC1JJkiRpjhjGpY0WvGmwBa7mlqF12U1yXJI1SS7qafvXJD9MckGSzyfZsW1flOSmJCva238NKy5JkiRJ0vQwzHNIjwcOHtd2OrBfVT0Y+DHw+p7nflJVi9vbK4YYlyRJkiRpGhhaQVpVZwC/Htd2WlXd1j48C9h9WK8vSZIkSZreuhxl9y+Ar/Q8vl+SHyT5VpJHdRWUJEmSJGk0OhnUKMk/ArcBn2ibrgb2rKpfJXkocEqSB1XV9RPMuwxYBrDlFhlVyJIkSZKkARv5EdIkRwBPB55XVQVQVTdX1a/a++cCPwEeMNH8VXVsVS2pqiVbWJBKkiRJ0ow10oI0ycHA64BnVtVve9rnJ9myvX9/YG/gp6OMTZIkSZI0WkPrspvkU8BBwC5JVgJvphlV9+7A6UkAzmpH1H008M9JbgNuB15RVb+ecMGSJEmSpFlhaAVpVR0+QfOHJ5n2ZODkYcUiSdJ0lGQP4KPAfYD1wLFV9d4kRwMvA9a2k76hqr7cTZSSJA1PJ4MaSZIkoBng7zVVdV6SecC5SU5vn3t3Vb2jw9gkSRo6C1JJkjpSVVfTjDRPVd2Q5FJgYbdRSZI0Ol1eh1SSJLWSLAL2B85um16V5IIkxyXZqbvIJEkaHgtSSZI6lmR7mrEUjmyvwf1+YC9gMc0R1HdOMt+yJMuTLF+7du1Ek0iSNK1ZkEqS1KEkW9MUo5+oqs8BVNU1VXV7Va0HPggsnWje3mtzz58/f3RBS5I0IBakkiR1JM010D4MXFpV7+pp361nsmcDF406NkmSRsFBjSRJ6s6BwAuAC5OsaNveAByeZDFQwJXAy7sITpKkYbMglSSpI1X1HSATPOU1RyVJc4JddiVJkiRJnbAglSRJkiR1wi67kiRJ0jS1dPG+rFy1amDLu3HdOmC7gS1P2lwWpJIkSdI0tXLVKlYfM7gCct6R1w9sWdIg2GVXkiRJktQJC1JJkiRJUicsSCVJkiRJnbAglSRJkiR1woJUkiRJktQJC1JJkiRJUif6KkiT7DfsQCRJmsnMlZIkTV2/R0j/K8k5Sf4qyY7DDEiSpBnKXClJ0hT1VZBW1SOB5wF7AMuTfDLJE4camSRJM4i5UpKkqev7HNKqugx4I/A64DHAvyX5YZL/M6zgJEmaScyVkiRNTb/nkD44ybuBS4HHAc+oqj9s7797iPFJkjQjmCslSZq6rfqc7n3AB4E3VNVNY41VtTrJG4cSmSRJM4u5UpKkKeq3IH0qcFNV3Q6QZAtgm6r6bVV9bGjRSZI0c5grJUmaon7PIf06sG3P43u0bZIkqWGulCRpivotSLepqnVjD9r79xhOSJIkzUjmSkmSpqjfgvTGJAeMPUjyUOCmDUwvSdJcY66UJGmK+j2H9EjgM0lWt493A567oRmSHAc8HVhTVfu1bTsDJwKLgCuB51TVte1zrwdeAtwOvLqqvjaVFZEkqWNHMsVcKUnSXNdXQVpV30/yQGAfIMAPq+rWjcx2PM2Igx/taTsK+EZVvS3JUe3j1yXZFzgMeBCwAPh6kgeMDQwhSdJ0t4m5UpKkOa3fI6QAD6M5srkVsH8Squqjk01cVWckWTSu+RDgoPb+CcA3aS4efgjw6aq6GbgiyeXAUuDMKcQnSVLXppQrJUma6/oqSJN8DNgLWEHTpRaguPPRz37sWlVXA1TV1Unu3bYvBM7qmW5l2yZJ0owwwFwpaQJLF+/LylWrBrrM3Rcu5JwVlwx0mXPRTTeuY8H8HQa2PP8vc0u/R0iXAPtWVQ0pjkzQNuFrJVkGLAPYcouJZpMkqRPDzpXSnLZy1SpWH7PdQJe54E2DLXDnqtvWrx/o/8b/y9zS7yi7FwH3GcDrXZNkN4D275q2fSWwR890uwOrmUBVHVtVS6pqyRYWpJKk6WNQuVKSpDmj3yOkuwCXJDkHuHmssaqeOcXXOxU4Anhb+/cLPe2fTPIumkGN9gbOmeKyJUnq0pRzZZI9aLr03gdYDxxbVe/d0Kj0kiTNJv0WpEdPdcFJPkUzgNEuSVYCb6YpRE9K8hLg58ChAFV1cZKTgEuA24BXOsKuJGmGOXoT5rkNeE1VnZdkHnBuktOBFzHBqPQDi1SSpGmi38u+fCvJfYG9q+rrSe4BbLmReQ6f5KnHTzL9W4G39hOPJEnTzSbmyquBscH+bkhyKc2gfpONSi9J0qzS1zmkSV4GfBb4QNu0EDhlSDFJkjTjbG6ubC+Vtj9wNuNGpQfuvYFZJUmasfod1OiVwIHA9QBVdRkmR0mSem1yrkyyPXAycGRVXd/vCyZZlmR5kuVr167dhJAlSepWvwXpzVV1y9iDJFsxyWVZJEmaozYpVybZmqYY/URVfa5tnmxU+jvpHXl+/vz5m70CkiSNWr8F6beSvAHYNskTgc8A/z28sCRJmnGmnCuTBPgwcGlVvavnqbFR6eHOo9JLkjSr9FuQHgWsBS4EXg58GXjjsIKSJGkG2pRceSDwAuBxSVa0t6fSjEr/xCSXAU9sH0uSNOv0O8rueuCD7U2SJI2zKbmyqr4DZJKnJxyVXpKk2aSvgjTJFUxwHkxV3X/gEUmSNAOZKyVJmrq+ClJgSc/9bYBDgZ0HH44kSTOWuVKSpCnq6xzSqvpVz21VVb0HeNxwQ5MkaeYwV0qSNHX9dtk9oOfhFjR7gecNJSJJkmYgc6UkSVPXb5fdd/bcvw24EnjOwKORJGnmMldKkjRF/Y6y+9hhByJJ0kxmrpQkaer67bL7dxt6ftzFvCVJmnPMlZIkTd1URtl9GHBq+/gZwBnAVcMISpKkGchcKUnSFPVbkO4CHFBVNwAkORr4TFW9dFiBSZI0w5grJUmaon4L0j2BW3oe3wIsGng0kiTNXOZKaY5bunhfVq5aNdBl3rhuHbDdQJcpTSf9FqQfA85J8nmggGcDHx1aVJIkzTzmSmmOW7lqFauPGWzxOO/I6we6PGm66XeU3bcm+QrwqLbpxVX1g+GFJUnSzGKulCRp6raYwrT3AK6vqvcCK5Pcb0gxSZI0U5krJUmagr4K0iRvBl4HvL5t2hr4+LCCkiRppjFXSpI0df0eIX028EzgRoCqWg3MG1ZQkiTNQOZKSZKmqN+C9JaqKppBGkjiUF+SJN2ZuVKSpCnqtyA9KckHgB2TvAz4OvDB4YUlSdKMY66UJGmKNjrKbpIAJwIPBK4H9gH+qapOH3JskiTNCOZKSZI2zUYL0qqqJKdU1UMBE6skSeOYKyVJ2jT9dtk9K8nDhhqJJEkzm7lSkqQp2ugR0tZjgVckuZJm9MDQ7BB+8LACkyRphjFXSpI0RRssSJPsWVU/B54yongkSZpRzJWSJG26jR0hPQU4oKp+luTkqvrTzX3BJPvQDPww5v7APwE7Ai8D1rbtb6iqL2/u60mSNGSnMOBcKUnSXLGxgjQ99+8/iBesqh8BiwGSbAmsAj4PvBh4d1W9YxCvI0nSiAw8V0qSNFdsbFCjmuT+oDwe+ElV/WwIy5YkaRSGnSslSZq1NnaE9CFJrqfZ+7ttex/uGKjhnpv5+ocBn+p5/KokLwSWA6+pqms3c/mSJA3bsHOlJEmz1gaPkFbVllV1z6qaV1VbtffHHm9Wgk1yN+CZwGfapvcDe9F0570aeOck8y1LsjzJ8vXr3REtSerWMHOlJEmzXb/XIR2GpwDnVdU1AFV1TVXdXlXrgQ8CSyeaqaqOraolVbVkiy0y0SSSJM0ISY5LsibJRT1tRydZlWRFe3tqlzFKkjRMXRakh9PTXTfJbj3PPRu46C5zSJI0uxwPHDxB+7uranF7c8R5SdKstbFzSIciyT2AJwIv72n+f0kW0wwIceW45yRJmnWq6owki7qOQ5KkrnRSkFbVb4F7jWt7QRexSJI0DTnInyRpTuiyy64kSbqrvgb5gzsP9Ld27doRhSdJ0uBYkEqSNI30O8hfO+3vB/qbP3/+6IKUJGlALEglSZpGHORPkjSXdHIOqSRJgiSfAg4CdkmyEngzcJCD/EmS5goLUkmSOlJVh0/Q/OGRByJJUkfssitJkiRJ6oQFqSRJkiSpExakkiRJkqROWJBKkiRJkjphQSpJkiRJ6oQFqSRJkiSpExakkiRJkqROWJBKkiRJkjphQSpJkiRJ6sRWXQcgSZIkdeGmG9exYP4OA1vejevWAdsNbHnSXGBBKkmSpDnptvXrWX3M4ArIeUdeP7BlSXOFXXYlSZIkSZ2wIJUkSZIkdcKCVJIkSZLUCQtSSZIkSVInLEglSZIkSZ1wlF1JkjRjLV28LytXrRrY8rxsx2AM+v8C/m+k2cqCVJIkzVgrV63ysh3T0KD/L+D/Rpqt7LIrSZIkSeqEBakkSZIkqRMWpJIkSZKkTliQSpIkSZI6YUEqSZIkSepEJ6PsJrkSuAG4HbitqpYk2Rk4EVgEXAk8p6qu7SI+SZIkSdLwdXmE9LFVtbiqlrSPjwK+UVV7A99oH0uSJEmSZqnp1GX3EOCE9v4JwLO6C0WSJEmSNGxdFaQFnJbk3CTL2rZdq+pqgPbvvTuKTZKkkUhyXJI1SS7qads5yelJLmv/7tRljJIkDVNXBemBVXUA8BTglUke3e+MSZYlWZ5k+fr1NbwIJUkavuOBg8e1eQqLJGnO6KQgrarV7d81wOeBpcA1SXYDaP+umWTeY6tqSVUt2WKLjCpkSZIGrqrOAH49rtlTWCRJc8bIC9Ik2yWZN3YfeBJwEXAqcEQ72RHAF0YdmyRJ04CnsEiS5owuLvuyK/D5JGOv/8mq+mqS7wMnJXkJ8HPg0A5ikyRpxmjHYVgGsOeee3YcjSRJUzfygrSqfgo8ZIL2XwGPH3U8kiRNM9ck2a2qrt7QKSzQnMYCHAuwZMkSB1aQJM040+myL5IkyVNYJElziAWpJEkdSfIp4ExgnyQr29NW3gY8McllwBPbx5IkzUpdnEMqSZKAqjp8kqc8hUWSNCd4hFSSJEmS1AkLUkmSJElSJyxIJUmSJEmdsCCVJEmSJHXCglSSJEmS1AkLUkmSJElSJyxIJUmSJEmdsCCVJEmSJHXCglSSJEmS1AkLUkmSJElSJyxIJUmSJEmdsCCVJEmSJHXCglSSJEmS1AkLUkmSJElSJyxIJUmSJEmdsCCVJEmSJHViq64DkCRJUrduunEdC+bvMLDl3bhuHbDdwJYnafayIJUkSZrjblu/ntXHDK6AnHfk9QNblqTZzS67kiRJkqROWJBKkiRJkjphQSpJkiRJ6oQFqSRJkiSpExakkiRJkqROOMquJEmSpGlj0JchAth94ULOWXHJQJepwbAgHWfQHwDf/JIkSVL/Bn0ZIoAFb1o10OVpcEZekCbZA/gocB9gPXBsVb03ydHAy4C17aRvqKovjzq+QX8AfPNLkiRJ0sS6OEJ6G/CaqjovyTzg3CSnt8+9u6re0UFMkiRJkqQRG3lBWlVXA1e3929IcimwcNRxSJI0nSW5ErgBuB24raqWdBuRJEmD1+kou0kWAfsDZ7dNr0pyQZLjkuzUXWSSJE0Lj62qxRajkqTZqrOCNMn2wMnAkVV1PfB+YC9gMc0R1HdOMt+yJMuTLF+/vkYVriRJkiRpwDopSJNsTVOMfqKqPgdQVddU1e1VtR74ILB0onmr6tiqWlJVS7bYIqMLWpKk0SrgtCTnJlk20QS9O2nXrl070SSSJE1rIy9IkwT4MHBpVb2rp323nsmeDVw06tgkSZpGDqyqA4CnAK9M8ujxE/TupJ0/f/7oI5QkaTN1McrugcALgAuTrGjb3gAcnmQxzR7hK4GXdxCbJEnTQlWtbv+uSfJ5mp5DZ3QblSRJg9XFKLvfASbqazvya45KkjQdJdkO2KIdjX474EnAP3ccliRJA9fFEVJJkrRhuwKfb85yYSvgk1X11W5DkiRp8CxIh+ymG9exYP4OA13m7gsXcs6KSwa6TEnS9FFVPwUe0nUckiQNmwXpkN22fj2rj9luoMtc8KZVA12eJEmSJHWhs+uQSpIkSZLmNgtSSZIkSVInLEglSZIkSZ2wIJUkSZIkdcKCVJIkSZLUCQtSSZIkSVInLEglSZIkSZ2wIJUkSZIkdcKCVJIkSZLUCQtSSZIkSVInLEglSZIkSZ3YqusANHU33biOBfN3GOgyd1+4kHNWXDLQZUqSJEnShliQzkC3rV/P6mO2G+gyF7xp1UCXJ0mSJEkbY5ddSZIkSVInPEIqYPDdgO0CLEmSJGljLEgFDL4bsF2AJUmSNF04Bsv0ZUEqSZIkaVZzDJbpy3NIJUmSJEmdsCCVJEmSJHXCglSSJEmS1AkLUkmSJElSJyxIJUmSJEmdcJRdSZI0EksX78vKVYMdlfLGdeuAwY6cKUmzxTC+dwfNglRz1jA+oF6PSpImt3LVqoFfdmHekdcPdHmSNJsM+ns3fzn471wLUs1Zw/hh5PWoJEmSpP5Nu3NIkxyc5EdJLk9yVNfxSJLUBfOhJGkumFZHSJNsCfwH8ERgJfD9JKdWlX0gNfAutnP1vCO7KkvTn/lQkjRXTKuCFFgKXF5VPwVI8mngEMAEPMPcdOM6FszfYaDLvHHdOn7z7l0HtrxhnHc06PW+9ZbfsfXdthnY8mDw2xFmRlflQRfic7UId4fGyJgPJUlzwnQrSBcCV/U8Xgk8vKNYtBluW79+Tg5cMej1nnfk9ax9+70GtryxZc5Fgz5neCYU4cPgudcjYz6UJM0JqaquY/i9JIcCT66ql7aPXwAsraq/7plmGbCsfbgP8KORBzq97QL8susgphG3x125Te7KbXJnbo+72qeq5o3qxfrJh217b07cD7hoVDF2bK68R+fKeoLrOlu5rrPPwPPhdDtCuhLYo+fx7sDq3gmq6ljg2FEGNZMkWV5VS7qOY7pwe9yV2+Su3CZ35va4qyTLR/ySG82HcOecOJf+b3NlXefKeoLrOlu5rrPPMPLhdBtl9/vA3knul+RuwGHAqR3HJEnSqJkPJUlzwrQ6QlpVtyV5FfA1YEvguKq6uOOwJEkaKfOhJGmumFYFKUBVfRn4ctdxzGB2Z74zt8dduU3uym1yZ26Puxr5NtmEfDiX/m9zZV3nynqC6zpbua6zz8DXc1oNaiRJkiRJmjum2zmkkiRJkqQ5woJ0hklyZZILk6wYG+Uqyc5JTk9yWft3p57pX5/k8iQ/SvLk7iIfnCTHJVmT5KKetilvgyQPbbfl5Un+LUlGvS6DMMn2ODrJqvZ9siLJU3uem9XbAyDJHkn+N8mlSS5O8jdt+5x8n2xge8zZ90mSbZKck+T8dpu8pW2fNu+RufRdN1c+szPhfTdoSbZM8oMkX2wfz8p1zYB+n82Qdd0xyWeT/LD9zP7xbFzXJPvkjvy4Isn1SY6cpev6t+130kVJPtV+V41uPavK2wy6AVcCu4xr+3/AUe39o4C3t/f3Bc4H7g7cD/gJsGXX6zCAbfBo4ADgos3ZBsA5wB8DAb4CPKXrdRvg9jgaeO0E08767dGuy27AAe39ecCP23Wfk++TDWyPOfs+aePfvr2/NXA28Ijp9B6ZS991c+UzOxPed0NY578DPgl8cZa/h69kAL/PZsi6ngC8tL1/N2DH2bquPeu8JfAL4L6zbV2BhcAVwLbt45OAF41yPT1COjscQvPlQPv3WT3tn66qm6vqCuByYOnowxusqjoD+PW45iltgyS7AfesqjOr+QR9tGeeGWWS7TGZWb89AKrq6qo6r71/A3ApzRfunHyfbGB7TGZWbw+AaqxrH27d3opp9B6ZS991c+UzOxPed4OUZHfgacCHeppn5bpOYtata5J70uws+zBAVd1SVdcxC9d1nMcDP6mqnzE713UrYNskWwH3oLnu9cjW04J05ingtCTnJlnWtu1aVVdDk9SBe7ftC4GreuZdyYZ/hM5kU90GC9v749tnk1cluSBNt7+xbhZzbnskWQTsT3MkYs6/T8ZtD5jD75O2K+EKYA1welXNhPfIdI9vs832z+wMfd9tqvcA/wCs72mbres6iN9nM2Fd7w+sBT6Spiv2h5Jsx+xc116HAZ9q78+qda2qVcA7gJ8DVwO/qarTGOF6WpDOPAdW1QHAU4BXJnn0BqadqN92DSesaWuybTDbt837gb2AxTRfLu9s2+fU9kiyPXAycGRVXb+hSSdom3XbZYLtMaffJ1V1e1UtBnan2bu73wYmn+7bZLrH15e58JmdZe+7SSV5OrCmqs7td5YJ2mbEurYG8ftsJqzrVjSnEry/qvYHbqTpzjmZmbyuACS5G/BM4DMbm3SCtmm/ru3O6ENout8uALZL8vwNzTJB22atpwXpDFNVq9u/a4DP03TBvaY9TE77d007+Upgj57Zd6c5BD8bTXUbrGzvj2+fFarqmvZHz3rgg9zRVXvObI8kW9P8sP1EVX2ubZ6z75OJtofvk0bb3eybwMFM//fIdI9vk821z+wMe99tigOBZya5Evg08LgkH2d2ruugfp/NhHVdCaxsj+wDfJamQJ2N6zrmKcB5VXVN+3i2resTgCuqam1V3Qp8DvgTRrieFqQzSJLtkswbuw88CbgIOBU4op3sCOAL7f1TgcOS3D3J/YC9aU42no2mtA3argc3JHlEOwLYC3vmmfHGvkBaz6Z5n8Ac2R7tOnwYuLSq3tXz1Jx8n0y2Peby+yTJ/CQ7tve3pUnIP2T6v0eme3ybZK58Zmfw+27Kqur1VbV7VS2i6e74P1X1fGbhug7q99lMWNeq+gVwVZJ92qbHA5cwC9e1x+Hc0V0XZt+6/hx4RJJ7tPE9nuY8/tGtZ02D0Z289T0K1v1pRrU6H7gY+Me2/V7AN4DL2r8798zzjzSjX/2IaTSi12Zuh0/RdC+8lWZvzEs2ZRsAS2gSxk+A9wHpet0GuD0+BlwIXNB+cew2V7ZHuy6PpOkmcgGwor09da6+TzawPebs+wR4MPCDdt0vAv6pbZ8275G59F03Vz6zM+F9N6T1Pog7RtmddevKAH+fTfd1bWNcDCxv38enADvN4nW9B/ArYIeetlm3rsBbaHaOXUTz2+Duo1zPtDNLkiRJkjRSdtmVJEmSJHXCglSSJEmS1AkLUkmSJElSJyxIJUmSJEmdsCCVJEmSJHXCglTaTEluT7IiyUVJPpPkHtMgpmcmOaq9f3ySP5tgmoOSfHGC6Z+VZN9NeM33JHn0FKZflOSmJD9IcmmSc5Ic0fP805O8ZapxSNJskeTZSSrJA0f0eouS/HnP4yVJ/m0K838zyZLhRDfh670oyftG9XrjXnv/JB9q7x+d5LWbsIxzk9ytz2mf2E5/Yfv3cX3M86L2f5r28aFJLk6yfvz/Kcnrk1ye5EdJnjzBsj6Q5MaJXjfJc5Jc0i77k23b/CRf7WfdJAtSafPdVFWLq2o/4BbgFf3MlGSrYQVUVadW1ds2cfpnAVMqSJPsDDyiqs6YynzAT6pq/6r6Q5oLp/9tkhe3z30JeOZ0KPAlqSOHA9+h+X4chUXA7wvSqlpeVa8e0WvPNG8A/n1TZ06yCFhVVbf0OcsvgWdU1R8BR9BcK3KyZS9M8mFgT5pr/P5X+9RFwP8Bzhg3/b4077EHAQcD/5lky57n30hzrdGHA/+R5ME9z+0NvB44sKoeBBwJUFVrgauTHNjn+mkOsyCVBuvbwB8k2S7JcUm+3x4BPAR+v7fyM0n+GzitfXxKkv9OckWSVyX5u3aes9pCjyR7Jflqu1f022N7y5M8I8nZ7fRfT7Jrz+v07jV+Qjvfj5M8fXzQY9Mn+RPgmcC/tkd990pyXs90eyc5d4L1/jPgqz3TXZnkX5KcmWR5kgOSfC3JT5JMWLBX1U+BvwNe3T4u4JvAXeKVpNkuyfbAgcBL6ClIk2yZ5B3tkbILkvx12/74Nhdc2Oafu7ftVybZpb2/JMk32/uPab/nV7TzzQPeBjyqbfvb3LknzfZJPtLzun+6kfjXJXl7m7e+nmRpmiOoP03yzHaaFyX5QpvffpTkzT3zPz9Nz5kV7dG5Ldv2F7e57Fvt9hmbfrJ8eHS7PcZe+9U987ywXZfzk3ysbZuf5OQ2f39/ooKq3VYPrqrzJ3juZUm+kmTbJA9rl39mkn9NclHPpE+hzZv9bKuq+kFVrW7nvRjYJsnd2/fD8Wl6aV2Y5G+rahVNwfwX7XvnL9tlXFpVP5rg33UI8OmqurmqrgAuB5a2sR0B7Af8eVVdRPMb4YNJ9mjnfRnwH1V1bfsaa3qWewrwvAleT7oTC1JpQNIc8XwKcCHwj8D/VNXDgMfSFHjbtZP+MXBEVY11e9mPZo/0UuCtwG+ran/gTOCF7TTHAn9dVQ8FXgv8Z9v+HZojk/sDnwb+YZLwFgGPAZ4G/FeSbSaaqKq+B5wK/H171PcnwG+SLG4neTFw/ASzHgiML1Svqqo/pinSj6cpWh8B/PMkMQKcB/R2TVsOPGoD00vSbPUs4KtV9WPg10kOaNuXAfcD9q+qBwOfaL/Tjwee2x5B24q2CNmA1wKvrKrFNN+zNwFHAd9uv//fPW76NwG/qao/al/3fzay/O2Ab7Z56wbg/wJPBJ7NnfPAUpqiZTFwaFs0/yHwXJqjbouB24HnJdkNeAtNznkid+7Ns6F8+EDgye1rvTnJ1kkeRJOrH1dVDwH+pp32vcC72/z9p8CHJli3JTRHG+8kyauAZwDPqqqbgI8Ar2hz4e3jJj+YO3bk9rutxvwp8IOqurndbgurar/2f/+RJAvaZRwHnAj8xwTL6LUQuKrn8cq2jao6oaoOq6rb2seXVdXDq2ps+gcAD0jy3TQ70g/uWY45XH0ZWpdBaQ7ZNsmK9v63gQ8D36Ppbjp2Tsk2NF1nAE6vql/3zP+/VXUDcEOS3wD/3bZfCDy43Uv+J8Bn0pwGAnD39u/uwIltkr4bcMUkMZ5UVeuBy5L8lDsXfRvzIeDFSf6O5gfC0gmm2Q1YO67t1J712L5nHX+XZMdJXivjHq8BFkwhVkmaLQ4H3tPe/3T7+DzgCcB/9RQIv07yEOCKtngFOAF4Zc/8E/ku8K4knwA+V1Ure3LMRJ5Az5HasSNiG3ALdxRcFwI3V9WtSS6k2Uk65vSq+hVAks/RdDG9DXgo8P02pm1p8sHDaQq3te30J9IURLDhfPiltni7OckaYFfgccBnq+qX7fqM5eUnAPv2bIt7JpnX5rAxE+W8F9AUcs9q13NHYF67oxfgk7Q9ftKcN7p72zNoKtuKtpB+O/CktumnwP2T/DvNqS6nVdV1wMuSvIjmd8nH2bCJ/vG1kXnGbAXsDRxE8z/4dpL92hjM4eqLBam0+W5q9+D+XppM9qfju8YkeThw47j5b+65v77n8Xqaz+gWwHXjX6P178C7qurUJAcBR08S4/jE0m+iATgZeDPN3vBzx344jHMTTdHdq3c9xq/jZN89+wOX9jzepl22JM0ZSe5FUzDtl6SALYFK8g80xcP47/ANVZK3cUePuN9/T1fV25J8CXgqcFaSJ2wsrAled0NubU+9gJ48UFXrc+cxFCbKTwFOqKrX3ymA5FkbiGFD+bA3B91Ok4MmW58tgD9uj3BOZqKcdxHN0crdaYrhDf1PHkVzRHdMX9sqye7A54EXtj2YqKpr2x0ST6bZCfEcmq66VNXxG4ih10pgj57HuwOrJ5l2onnPqqpbgSuS/IimQP0+5nD1yS670nB8DfjrtjAlyf6buqCqup7mS/7Qdllpkw/ADsCq9v4RE83fOjTJFkn2Au4PTHQOyZgbgHk9r/87mvV5P033o4lcCvzBxtZlQ9IM8PAO7jxIxAOYoFuUJM1yfwZ8tKruW1WLqmoPmiLnkcBpwCvGCpU0Yw38EFiUZOx7+AXAt9r7V9IcbYSmqyftfHtV1YVV9XaarpUPZNz3/zinAa/qmX+nzV7LxhOT7JxkW5puyt8FvgH8WZJ7t6+1c5L7AmcDByW5V5KtgUN7ltNvPhzzDeA5bfE/th3hruu5eIJ5J8p5PwBeDpyaZEF7BPmGJI9on+8dmOpg4Ct9xPh77RHXLwGvr6rv9rTvAmxRVSfTdKs+YOIlbNCpwGHtOan3oykoz+lz3lNoTk0ai+UBNEdtwRyuPlmQSsNxDLA1cEE7iMExm7m85wEvSXI+zWAGh7TtR9N05f02zQh8k/kRzY+Tr9Ccz/K7DUz7aeDv0wwMsVfb9gmaPcmnTTLPl2i660zVXu3rXAqcBPx7VfUWvY9tly1Jc8nhNEfCep1MM97Ah4Cf0+SX82kGm/kdzTn+n2m7ea7njpFV3wK8t80TvecxHtkOhHM+zVGsrwAXALelGeTnb8e9/v8FduqZ57EDWtfv0IwYuwI4uR3Z9xLgjTSD/10AnA7sVlVX0+S9M4Gv03RhHnM0/eVDAKrqYppxG77Vrs+72qdeDSxJMxjRJUwwcn5V/RDYIc3gRr3t36E5N/dLbXH2EuDYJGfSHDH9TTvpQdyxw6Bfr6Ipgt+UOwajujfNuZ7fbE8dOp5mxNsJpbmM0EqasSy+lORrPdviJOASmq7Dr6yq8ee8TuZrwK/abfW/NGNQjPWkMoerL7mjh4AkTaw9F3aHqnrTBqb5DvD09ryRQbzmrsAnq+rxg1ieJGl6ac9xXFJVr9rYtNNNW7DfUFUTDXo0Ns32VbWuvX8Uzbmn/wp8sKqeMppIu5PkDOCQPs431hznOaSSNijJ54G9aM5n2pDX0AzcdN2AXnrPdpmSJE037+fOXYYn8rQkr6f5vf0z4EXtgExzoRidT3NOr8WoNsojpJIkSZKkTngOqSRJkiSpExakkiRJkqROWJBKkiRJkjphQSpJkiRJ6oQFqSRJkiSpExakkiRJkqRO/P/So5TifC0fHQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "permmin = 0.01; permmax = 3000;                # user specified min and max\n",
    "AImin = 1000.0; AImax = 8000\n",
    "Fmin = 0; Fmax = 1\n",
    "\n",
    "plt.subplot(221)\n",
    "GSLIB.hist_st(df['Facies'].values,Fmin,Fmax,log=False,cumul = False,bins=20,weights = None,xlabel='Facies (1-sand, 0-shale)',title='Facies Well Data')\n",
    "\n",
    "plt.subplot(222)\n",
    "GSLIB.hist_st(df['Porosity'].values,pormin,pormax,log=False,cumul = False,bins=20,weights = None,xlabel='Porosity (fraction)',title='Porosity Well Data')\n",
    "\n",
    "plt.subplot(223)\n",
    "GSLIB.hist_st(df['Perm'].values,permmin,permmax,log=False,cumul = False,bins=20,weights = None,xlabel='Permeaiblity (mD)',title='Permeability Well Data')\n",
    "\n",
    "plt.subplot(224)\n",
    "GSLIB.hist_st(df['AI'].values,AImin,AImax,log=False,cumul = False,bins=20,weights = None,xlabel='Acoustic Impedance (kg/m2s*10^6)',title='Acoustic Impedance Well Data')\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=2.1, wspace=0.1, hspace=0.2)\n",
    "#plt.savefig('hist_Porosity_Multiple_bins.tif',dpi=600,bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comments\n",
    "\n",
    "This was a basic demonstration of calculating univariate statistics and visualizing data distributions. Much more could be done, I have other demosntrations on basics of working with DataFrames, ndarrays and many other workflows availble at https://github.com/GeostatsGuy/PythonNumericalDemos and https://github.com/GeostatsGuy/GeostatsPy.\n",
    "\n",
    "I hope this was helpful,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "#### The Author:\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*\n",
    "\n",
    "With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development. \n",
    "\n",
    "For more about Michael check out these links:\n",
    "\n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "#### Want to Work Together?\n",
    "\n",
    "I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.\n",
    "\n",
    "* Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you! \n",
    "\n",
    "* Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!\n",
    "\n",
    "* I can be reached at mpyrcz@austin.utexas.edu.\n",
    "\n",
    "I'm always happy to discuss,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
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